12/16/2022 15:27:11 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True 12/16/2022 15:27:11 - 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=500, 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=2, gradient_checkpointing=True, greater_is_better=False, group_by_length=False, half_precision_backend=auto, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=1e-05, length_column_name=input_length, load_best_model_at_end=True, local_rank=-1, log_level=passive, log_level_replica=passive, log_on_each_node=True, logging_dir=./runs/Dec16_15-27-11_129-146-67-7, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=25, logging_strategy=steps, lr_scheduler_type=linear, max_grad_norm=1.0, max_steps=10000, metric_for_best_model=wer, mp_parameters=, no_cuda=False, num_train_epochs=3.0, optim=adamw_hf, optim_args=None, output_dir=./, overwrite_output_dir=True, past_index=-1, per_device_eval_batch_size=32, per_device_train_batch_size=32, predict_with_generate=True, prediction_loss_only=False, push_to_hub=True, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, 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, ) 12/16/2022 15:27:11 - 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=500, 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=2, gradient_checkpointing=True, greater_is_better=False, group_by_length=False, half_precision_backend=auto, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=1e-05, length_column_name=input_length, load_best_model_at_end=True, local_rank=-1, log_level=passive, log_level_replica=passive, log_on_each_node=True, logging_dir=./runs/Dec16_15-27-11_129-146-67-7, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=25, logging_strategy=steps, lr_scheduler_type=linear, max_grad_norm=1.0, max_steps=10000, metric_for_best_model=wer, mp_parameters=, no_cuda=False, num_train_epochs=3.0, optim=adamw_hf, optim_args=None, output_dir=./, overwrite_output_dir=True, past_index=-1, per_device_eval_batch_size=32, per_device_train_batch_size=32, predict_with_generate=True, prediction_loss_only=False, push_to_hub=True, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, 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, ) 12/16/2022 15:27:12 - INFO - datasets.builder - No config specified, defaulting to the single config: samromur_asr/samromur_asr 12/16/2022 15:27:12 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--samromur_asr/9f4b7a0674ad04192814f8e840dbdab23e8c4531498341198a39447a7e64e3b7 language-and-voice-lab/samromur_asr None train 2 12/16/2022 15:27:14 - INFO - datasets.builder - No config specified, defaulting to the single config: althingi_asr/althingi_asr 12/16/2022 15:27:14 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--althingi_asr/4fdbd9c08f7d073eeb89906cb5aa0e1e6bab822aa994474c639f8e8442a61bcf language-and-voice-lab/althingi_asr None train 13 12/16/2022 15:27:16 - INFO - datasets.builder - No config specified, defaulting to the single config: malromur_asr/malromur_asr 12/16/2022 15:27:16 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--malromur_asr/5084bd18db918163bfb6f3f09550dff5c0106bfc56985978e6f682f52690816d language-and-voice-lab/malromur_asr None train 2 12/16/2022 15:27:18 - INFO - datasets.builder - No config specified, defaulting to the single config: samromur_children/samromur_children 12/16/2022 15:27:18 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--samromur_children/0cde4367323adbaea6845954c908aec8bcd001520628d49577421d0776d9939d language-and-voice-lab/samromur_children None train 3 12/16/2022 15:27:20 - INFO - datasets.builder - No config specified, defaulting to the single config: raddromur_asr/raddromur_asr 12/16/2022 15:27:20 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--raddromur_asr/ad967117dc1846f4100ee0ecfc31859ea6743068904a5ee705ebc400b10c49d7 language-and-voice-lab/raddromur_asr None train 1 12/16/2022 15:27:22 - INFO - datasets.builder - No config specified, defaulting to the single config: samromur_asr/samromur_asr 12/16/2022 15:27:22 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/language-and-voice-lab--samromur_asr/9f4b7a0674ad04192814f8e840dbdab23e8c4531498341198a39447a7e64e3b7 [INFO|configuration_utils.py:654] 2022-12-16 15:27:23,444 >> loading configuration file config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/config.json [INFO|configuration_utils.py:706] 2022-12-16 15:27:23,448 >> Model config WhisperConfig { "_name_or_path": "openai/whisper-medium", "activation_dropout": 0.0, "activation_function": "gelu", "architectures": [ "WhisperForConditionalGeneration" ], "attention_dropout": 0.0, "begin_suppress_tokens": [ 220, 50257 ], "bos_token_id": 50257, "d_model": 1024, "decoder_attention_heads": 16, "decoder_ffn_dim": 4096, "decoder_layerdrop": 0.0, "decoder_layers": 24, "decoder_start_token_id": 50258, "dropout": 0.0, "encoder_attention_heads": 16, "encoder_ffn_dim": 4096, "encoder_layerdrop": 0.0, "encoder_layers": 24, "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": 24, "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 } [INFO|feature_extraction_utils.py:464] 2022-12-16 15:27:23,687 >> loading configuration file preprocessor_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/preprocessor_config.json [INFO|feature_extraction_utils.py:501] 2022-12-16 15:27:23,703 >> Feature extractor WhisperFeatureExtractor { "chunk_length": 30, "feature_extractor_type": "WhisperFeatureExtractor", "feature_size": 80, "hop_length": 160, "mel_filters": [ [ -0.0, 0.02486259490251541, 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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, 0.000366741674952209, 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:1799] 2022-12-16 15:27:23,942 >> loading file vocab.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/vocab.json [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file merges.txt from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/merges.txt [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file normalizer.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/normalizer.json [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file added_tokens.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/added_tokens.json [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/special_tokens_map.json [INFO|tokenization_utils_base.py:1799] 2022-12-16 15:27:23,942 >> loading file tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/tokenizer_config.json [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|endoftext|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|startoftranscript|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|en|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|zh|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|de|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|es|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|ru|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|ko|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|fr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|ja|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|pt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,004 >> Adding <|tr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|pl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ca|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|nl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ar|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|sv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|it|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|id|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|hi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|fi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|vi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|iw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|uk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|el|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ms|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|cs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ro|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|da|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|hu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ta|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|no|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|th|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ur|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|hr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|bg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|lt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|la|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|mi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|ml|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|cy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,005 >> Adding <|sk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|te|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|fa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|lv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|bn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|sr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|az|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|sl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|kn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|et|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|mk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|br|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|eu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|is|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|hy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|ne|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|mn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|bs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|kk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|sq|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|sw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|gl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|mr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|pa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|si|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|km|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|sn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|yo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|so|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|af|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|oc|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,006 >> Adding <|ka|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|be|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|tg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|sd|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|gu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|am|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|yi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|lo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|uz|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|fo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|ht|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|ps|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|tk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|nn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|mt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|sa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|lb|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|my|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|bo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|tl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|mg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|as|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|tt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|haw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|ln|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|ha|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|ba|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|jw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|su|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|translate|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,007 >> Adding <|transcribe|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,008 >> Adding <|startoflm|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,008 >> Adding <|startofprev|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,008 >> Adding <|nocaptions|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:24,008 >> Adding <|notimestamps|> to the vocabulary [INFO|modeling_utils.py:2251] 2022-12-16 15:27:24,011 >> loading weights file pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-medium/snapshots/a0b3589e1034234495a1b696c28d4832cdaf8a32/pytorch_model.bin [INFO|modeling_utils.py:2806] 2022-12-16 15:27:32,532 >> All model checkpoint weights were used when initializing WhisperForConditionalGeneration. [INFO|modeling_utils.py:2814] 2022-12-16 15:27:32,532 >> All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at openai/whisper-medium. 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] 2022-12-16 15:27:34,676 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 15:27:34,676 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 15:27:34,676 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 15:27:34,677 >> added tokens file saved in ./added_tokens.json [INFO|configuration_utils.py:447] 2022-12-16 15:27:34,758 >> Configuration saved in ./config.json [INFO|image_processing_utils.py:294] 2022-12-16 15:27:34,760 >> loading configuration file ./preprocessor_config.json [INFO|feature_extraction_utils.py:462] 2022-12-16 15:27:34,765 >> loading configuration file ./preprocessor_config.json [INFO|feature_extraction_utils.py:501] 2022-12-16 15:27:34,780 >> Feature extractor WhisperFeatureExtractor { "chunk_length": 30, "feature_extractor_type": "WhisperFeatureExtractor", 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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] 2022-12-16 15:27:34,780 >> loading file vocab.json [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file merges.txt [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file normalizer.json [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:1797] 2022-12-16 15:27:34,780 >> loading file tokenizer_config.json [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|endoftext|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|startoftranscript|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|en|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|zh|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|de|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|es|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ru|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ko|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|fr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ja|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|pt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|tr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|pl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ca|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|nl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ar|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|sv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|it|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|id|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|hi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|fi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|vi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|iw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|uk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|el|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,835 >> Adding <|ms|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|cs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|ro|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|da|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|hu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|ta|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|no|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|th|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|ur|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|hr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|bg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|lt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|la|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|mi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|ml|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|cy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|sk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|te|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|fa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|lv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|bn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|sr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|az|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|sl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|kn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|et|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|mk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|br|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|eu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,836 >> Adding <|is|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|hy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|ne|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|mn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|bs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|kk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|sq|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|sw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|gl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|mr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|pa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|si|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|km|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|sn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|yo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|so|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|af|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|oc|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|ka|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|be|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|tg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|sd|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|gu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|am|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|yi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|lo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|uz|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|fo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|ht|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|ps|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|tk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,837 >> Adding <|nn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|mt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|sa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|lb|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|my|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|bo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|tl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|mg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|as|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|tt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|haw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|ln|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|ha|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|ba|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|jw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|su|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|translate|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|transcribe|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|startoflm|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|startofprev|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|nocaptions|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-16 15:27:34,838 >> Adding <|notimestamps|> to the vocabulary /home/ubuntu/whipser-medium-is/./ is already a clone of https://huggingface.co/DavidErikMollberg/whipser-medium-is. Make sure you pull the latest changes with `repo.git_pull()`. 12/16/2022 15:27:37 - WARNING - huggingface_hub.repository - /home/ubuntu/whipser-medium-is/./ is already a clone of https://huggingface.co/DavidErikMollberg/whipser-medium-is. Make sure you pull the latest changes with `repo.git_pull()`. [INFO|trainer.py:511] 2022-12-16 15:27:40,322 >> max_steps is given, it will override any value given in num_train_epochs [INFO|trainer.py:565] 2022-12-16 15:27:40,322 >> Using cuda_amp half precision backend /home/ubuntu/whisper/.venv/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] 2022-12-16 15:27:40,343 >> ***** Running training ***** [INFO|trainer.py:1642] 2022-12-16 15:27:40,343 >> Num examples = 640000 [INFO|trainer.py:1643] 2022-12-16 15:27:40,343 >> Num Epochs = 9223372036854775807 [INFO|trainer.py:1644] 2022-12-16 15:27:40,344 >> Instantaneous batch size per device = 32 [INFO|trainer.py:1645] 2022-12-16 15:27:40,344 >> Total train batch size (w. parallel, distributed & accumulation) = 64 [INFO|trainer.py:1646] 2022-12-16 15:27:40,344 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1647] 2022-12-16 15:27:40,344 >> Total optimization steps = 10000 [INFO|trainer.py:1648] 2022-12-16 15:27:40,346 >> Number of trainable parameters = 763857920 0%| | 0/10000 [00:00> The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 0%| | 1/10000 [00:53<147:22:18, 53.06s/it] 0%| | 2/10000 [01:05<81:09:02, 29.22s/it] 0%| | 3/10000 [01:16<58:15:44, 20.98s/it] 0%| | 4/10000 [01:29<49:11:13, 17.71s/it] 0%| | 5/10000 [01:40<42:55:55, 15.46s/it] 0%| | 6/10000 [01:53<39:47:53, 14.34s/it] 0%| | 7/10000 [02:05<37:49:58, 13.63s/it] 0%| | 8/10000 [02:17<36:15:13, 13.06s/it] 0%| | 9/10000 [02:29<35:34:51, 12.82s/it] 0%| | 10/10000 [02:41<35:00:55, 12.62s/it] 0%| | 11/10000 [02:53<34:15:54, 12.35s/it] 0%| | 12/10000 [03:05<34:10:57, 12.32s/it] 0%| | 13/10000 [03:17<33:56:13, 12.23s/it] 0%| | 14/10000 [03:30<34:23:57, 12.40s/it] 0%| | 15/10000 [03:41<33:41:55, 12.15s/it][WARNING|tokenization_utils_base.py:3553] 2022-12-16 15:31:29,714 >> Token indices sequence length is longer than the specified maximum sequence length for this model (1057 > 1024). 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If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 5%|▌ | 500/10000 [1:54:54<33:23:44, 12.66s/it] 5%|▌ | 501/10000 [1:55:06<634:45:23, 240.56s/it] 5%|▌ | 502/10000 [1:55:18<453:21:33, 171.84s/it] 5%|▌ | 503/10000 [1:55:30<326:43:33, 123.85s/it] 5%|▌ | 504/10000 [1:55:42<238:27:48, 90.40s/it] 5%|▌ | 505/10000 [1:55:53<175:51:52, 66.68s/it] 5%|▌ | 506/10000 [1:56:04<131:46:20, 49.97s/it] 5%|▌ | 507/10000 [1:56:16<101:34:07, 38.52s/it] 5%|▌ | 508/10000 [1:56:28<80:28:19, 30.52s/it] 5%|▌ | 509/10000 [1:56:40<65:34:47, 24.87s/it] 5%|▌ | 510/10000 [1:56:51<55:15:21, 20.96s/it] 5%|▌ | 511/10000 [1:57:04<48:49:46, 18.53s/it] 5%|▌ | 512/10000 [1:57:16<43:01:14, 16.32s/it] 5%|▌ | 513/10000 [1:57:28<39:53:36, 15.14s/it] 5%|▌ | 514/10000 [1:57:39<36:46:04, 13.95s/it] 5%|▌ | 515/10000 [1:57:50<34:34:51, 13.13s/it] 5%|▌ | 516/10000 [1:58:02<33:33:47, 12.74s/it] 5%|▌ | 517/10000 [1:58:14<32:55:54, 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[2:26:45<30:09:15, 11.62s/it] 7%|▋ | 660/10000 [2:26:57<30:43:12, 11.84s/it] 7%|▋ | 661/10000 [2:27:09<31:09:11, 12.01s/it] 7%|▋ | 662/10000 [2:27:21<31:10:07, 12.02s/it] 7%|▋ | 663/10000 [2:27:34<31:37:16, 12.19s/it] 7%|▋ | 664/10000 [2:27:45<30:57:07, 11.94s/it] 7%|▋ | 665/10000 [2:27:59<32:21:31, 12.48s/it] 7%|▋ | 666/10000 [2:28:11<32:03:38, 12.37s/it] 7%|▋ | 667/10000 [2:28:23<31:56:45, 12.32s/it] 7%|▋ | 668/10000 [2:28:36<31:50:49, 12.29s/it] 7%|▋ | 669/10000 [2:28:47<31:16:42, 12.07s/it] 7%|▋ | 670/10000 [2:29:00<31:36:27, 12.20s/it] 7%|▋ | 671/10000 [2:29:12<31:48:03, 12.27s/it] 7%|▋ | 672/10000 [2:29:25<32:23:42, 12.50s/it] 7%|▋ | 673/10000 [2:29:37<31:45:35, 12.26s/it] 7%|▋ | 674/10000 [2:29:49<31:43:27, 12.25s/it] 7%|▋ | 675/10000 [2:30:01<31:33:59, 12.19s/it] 7%|▋ | 675/10000 [2:30:01<31:33:59, 12.19s/it] 7%|▋ | 676/10000 [2:30:13<31:21:32, 12.11s/it] 7%|▋ | 677/10000 [2:30:27<32:41:28, 12.62s/it] 7%|▋ | 678/10000 [2:30:40<32:51:19, 12.69s/it] 7%|▋ | 679/10000 [2:30:52<32:24:57, 12.52s/it] 7%|▋ | 680/10000 [2:31:05<32:52:18, 12.70s/it] 7%|▋ | 681/10000 [2:31:17<31:59:52, 12.36s/it] 7%|▋ | 682/10000 [2:31:30<32:30:29, 12.56s/it] 7%|▋ | 683/10000 [2:31:42<32:40:10, 12.62s/it] 7%|▋ | 684/10000 [2:31:54<32:14:50, 12.46s/it] 7%|▋ | 685/10000 [2:32:07<31:57:50, 12.35s/it] 7%|▋ | 686/10000 [2:32:18<31:27:26, 12.16s/it] 7%|▋ | 687/10000 [2:32:30<31:31:21, 12.19s/it] 7%|▋ | 688/10000 [2:32:44<32:36:13, 12.60s/it] 7%|▋ | 689/10000 [2:32:55<31:40:37, 12.25s/it] 7%|▋ | 690/10000 [2:33:07<31:25:02, 12.15s/it] 7%|▋ | 691/10000 [2:33:19<31:08:08, 12.04s/it] 7%|▋ | 692/10000 [2:33:31<31:02:45, 12.01s/it] 7%|▋ | 693/10000 [2:33:43<31:08:56, 12.05s/it] 7%|▋ | 694/10000 [2:33:56<31:55:05, 12.35s/it] 7%|▋ | 695/10000 [2:34:09<32:12:15, 12.46s/it] 7%|▋ | 696/10000 [2:34:21<32:07:28, 12.43s/it] 7%|▋ | 697/10000 [2:34:34<32:01:52, 12.40s/it] 7%|▋ | 698/10000 [2:34:46<31:39:49, 12.25s/it] 7%|▋ | 699/10000 [2:34:58<31:33:54, 12.22s/it] 7%|▋ | 700/10000 [2:35:12<33:28:41, 12.96s/it] 7%|▋ | 700/10000 [2:35:12<33:28:41, 12.96s/it] 7%|▋ | 701/10000 [2:35:25<33:16:06, 12.88s/it] 7%|▋ | 702/10000 [2:35:37<32:15:38, 12.49s/it] 7%|▋ | 703/10000 [2:35:48<31:42:17, 12.28s/it] 7%|▋ | 704/10000 [2:36:01<31:54:58, 12.36s/it] 7%|▋ | 705/10000 [2:36:13<31:30:15, 12.20s/it] 7%|▋ | 706/10000 [2:36:25<31:21:47, 12.15s/it] 7%|▋ | 707/10000 [2:36:37<31:24:46, 12.17s/it] 7%|▋ | 708/10000 [2:36:49<31:24:39, 12.17s/it] 7%|▋ | 709/10000 [2:37:00<30:36:01, 11.86s/it] 7%|▋ | 710/10000 [2:37:13<31:25:17, 12.18s/it] 7%|▋ | 711/10000 [2:37:25<31:08:35, 12.07s/it] 7%|▋ | 712/10000 [2:37:36<30:13:04, 11.71s/it] 7%|▋ | 713/10000 [2:37:48<30:05:18, 11.66s/it] 7%|▋ | 714/10000 [2:37:59<29:41:45, 11.51s/it] 7%|▋ | 715/10000 [2:38:10<29:39:43, 11.50s/it] 7%|▋ | 716/10000 [2:38:22<29:35:51, 11.48s/it] 7%|▋ | 717/10000 [2:38:34<30:14:21, 11.73s/it] 7%|▋ | 718/10000 [2:38:46<30:25:59, 11.80s/it] 7%|▋ | 719/10000 [2:38:57<30:05:37, 11.67s/it] 7%|▋ | 720/10000 [2:39:09<29:43:43, 11.53s/it] 7%|▋ | 721/10000 [2:39:20<29:31:48, 11.46s/it] 7%|▋ | 722/10000 [2:39:33<30:33:36, 11.86s/it] 7%|▋ | 723/10000 [2:39:45<31:19:25, 12.16s/it] 7%|▋ | 724/10000 [2:39:57<30:40:59, 11.91s/it] 7%|▋ | 725/10000 [2:40:09<30:39:34, 11.90s/it] 7%|▋ | 725/10000 [2:40:09<30:39:34, 11.90s/it] 7%|▋ | 726/10000 [2:40:22<31:33:31, 12.25s/it] 7%|▋ | 727/10000 [2:40:34<31:37:32, 12.28s/it] 7%|▋ | 728/10000 [2:40:45<30:58:13, 12.02s/it] 7%|▋ | 729/10000 [2:40:56<30:00:23, 11.65s/it] 7%|▋ | 730/10000 [2:41:08<29:58:16, 11.64s/it] 7%|▋ | 731/10000 [2:41:18<29:09:53, 11.33s/it] 7%|▋ | 732/10000 [2:41:30<29:18:31, 11.38s/it] 7%|▋ | 733/10000 [2:41:41<29:14:54, 11.36s/it] 7%|▋ | 734/10000 [2:41:52<29:00:24, 11.27s/it] 7%|▋ | 735/10000 [2:42:05<30:13:51, 11.75s/it] 7%|▋ | 736/10000 [2:42:17<30:19:06, 11.78s/it] 7%|▋ | 737/10000 [2:42:29<30:27:50, 11.84s/it] 7%|▋ | 738/10000 [2:42:42<31:26:31, 12.22s/it] 7%|▋ | 739/10000 [2:42:53<30:28:24, 11.85s/it] 7%|▋ | 740/10000 [2:43:05<30:47:36, 11.97s/it] 7%|▋ | 741/10000 [2:43:17<30:19:13, 11.79s/it] 7%|▋ | 742/10000 [2:43:30<31:27:53, 12.24s/it] 7%|▋ | 743/10000 [2:43:42<31:26:29, 12.23s/it] 7%|▋ | 744/10000 [2:43:56<32:27:02, 12.62s/it] 7%|▋ | 745/10000 [2:44:07<31:21:45, 12.20s/it] 7%|▋ | 746/10000 [2:44:18<30:21:25, 11.81s/it] 7%|▋ | 747/10000 [2:44:30<30:18:04, 11.79s/it] 7%|▋ | 748/10000 [2:44:41<30:01:24, 11.68s/it] 7%|▋ | 749/10000 [2:44:53<29:56:04, 11.65s/it] 8%|▊ | 750/10000 [2:45:04<29:57:58, 11.66s/it] 8%|▊ | 750/10000 [2:45:04<29:57:58, 11.66s/it] 8%|▊ | 751/10000 [2:45:16<30:05:14, 11.71s/it] 8%|▊ | 752/10000 [2:45:29<30:48:25, 11.99s/it] 8%|▊ | 753/10000 [2:45:41<30:46:30, 11.98s/it] 8%|▊ | 754/10000 [2:45:52<30:17:04, 11.79s/it] 8%|▊ | 755/10000 [2:46:04<30:00:08, 11.68s/it] 8%|▊ | 756/10000 [2:46:15<30:04:27, 11.71s/it] 8%|▊ | 757/10000 [2:46:27<30:03:20, 11.71s/it] 8%|▊ | 758/10000 [2:46:38<29:46:52, 11.60s/it] 8%|▊ | 759/10000 [2:46:51<30:15:55, 11.79s/it] 8%|▊ | 760/10000 [2:47:03<30:43:04, 11.97s/it] 8%|▊ | 761/10000 [2:47:14<30:19:18, 11.81s/it] 8%|▊ | 762/10000 [2:47:27<31:04:10, 12.11s/it] 8%|▊ | 763/10000 [2:47:39<31:04:04, 12.11s/it] 8%|▊ | 764/10000 [2:47:51<30:51:58, 12.03s/it] 8%|▊ | 765/10000 [2:48:03<30:59:05, 12.08s/it] 8%|▊ | 766/10000 [2:48:16<31:25:21, 12.25s/it] 8%|▊ | 767/10000 [2:48:29<31:58:06, 12.46s/it] 8%|▊ | 768/10000 [2:48:42<32:36:22, 12.71s/it] 8%|▊ | 769/10000 [2:48:55<32:22:13, 12.62s/it] 8%|▊ | 770/10000 [2:49:06<31:40:09, 12.35s/it] 8%|▊ | 771/10000 [2:49:18<31:19:00, 12.22s/it] 8%|▊ | 772/10000 [2:49:31<31:18:28, 12.21s/it] 8%|▊ | 773/10000 [2:49:43<31:06:27, 12.14s/it] 8%|▊ | 774/10000 [2:49:55<30:59:44, 12.09s/it] 8%|▊ | 775/10000 [2:50:08<31:50:10, 12.42s/it] 8%|▊ | 775/10000 [2:50:08<31:50:10, 12.42s/it] 8%|▊ | 776/10000 [2:50:21<32:10:09, 12.56s/it] 8%|▊ | 777/10000 [2:50:32<31:40:54, 12.37s/it] 8%|▊ | 778/10000 [2:50:45<31:43:00, 12.38s/it] 8%|▊ | 779/10000 [2:50:57<31:40:01, 12.36s/it] 8%|▊ | 780/10000 [2:51:09<31:18:33, 12.22s/it] 8%|▊ | 781/10000 [2:51:22<31:37:16, 12.35s/it] 8%|▊ | 782/10000 [2:51:34<31:16:49, 12.22s/it] 8%|▊ | 783/10000 [2:51:47<32:17:20, 12.61s/it] 8%|▊ | 784/10000 [2:51:59<31:28:41, 12.30s/it] 8%|▊ | 785/10000 [2:52:11<31:41:01, 12.38s/it] 8%|▊ | 786/10000 [2:52:23<31:29:46, 12.31s/it] 8%|▊ | 787/10000 [2:52:36<31:32:05, 12.32s/it] 8%|▊ | 788/10000 [2:52:49<32:03:13, 12.53s/it] 8%|▊ | 789/10000 [2:53:01<32:04:09, 12.53s/it] 8%|▊ | 790/10000 [2:53:13<31:09:12, 12.18s/it] 8%|▊ | 791/10000 [2:53:26<32:08:48, 12.57s/it] 8%|▊ | 792/10000 [2:53:38<31:39:50, 12.38s/it] 8%|▊ | 793/10000 [2:53:51<32:02:31, 12.53s/it] 8%|▊ | 794/10000 [2:54:03<31:40:36, 12.39s/it] 8%|▊ | 795/10000 [2:54:15<31:03:17, 12.15s/it] 8%|▊ | 796/10000 [2:54:28<31:41:13, 12.39s/it] 8%|▊ | 797/10000 [2:54:40<31:25:53, 12.30s/it] 8%|▊ | 798/10000 [2:54:52<31:33:11, 12.34s/it] 8%|▊ | 799/10000 [2:55:05<31:36:48, 12.37s/it] 8%|▊ | 800/10000 [2:55:17<31:25:17, 12.30s/it] 8%|▊ | 800/10000 [2:55:17<31:25:17, 12.30s/it] 8%|▊ | 801/10000 [2:55:29<31:24:10, 12.29s/it] 8%|▊ | 802/10000 [2:55:41<31:17:06, 12.24s/it] 8%|▊ | 803/10000 [2:55:53<31:14:29, 12.23s/it] 8%|▊ | 804/10000 [2:56:06<31:25:44, 12.30s/it] 8%|▊ | 805/10000 [2:56:19<32:18:53, 12.65s/it] 8%|▊ | 806/10000 [2:56:31<31:38:18, 12.39s/it] 8%|▊ | 807/10000 [2:56:44<32:26:12, 12.70s/it] 8%|▊ | 808/10000 [2:56:56<31:35:47, 12.37s/it] 8%|▊ | 809/10000 [2:57:08<31:36:00, 12.38s/it] 8%|▊ | 810/10000 [2:57:20<31:15:16, 12.24s/it] 8%|▊ | 811/10000 [2:57:33<31:42:31, 12.42s/it] 8%|▊ | 812/10000 [2:57:46<31:57:33, 12.52s/it] 8%|▊ | 813/10000 [2:57:59<32:08:23, 12.59s/it] 8%|▊ | 814/10000 [2:58:12<32:30:30, 12.74s/it] 8%|▊ | 815/10000 [2:58:25<32:27:10, 12.72s/it] 8%|▊ | 816/10000 [2:58:37<31:53:43, 12.50s/it] 8%|▊ | 817/10000 [2:58:49<32:05:34, 12.58s/it] 8%|▊ | 818/10000 [2:59:01<31:13:23, 12.24s/it] 8%|▊ | 819/10000 [2:59:12<30:49:17, 12.09s/it] 8%|▊ | 820/10000 [2:59:25<31:13:00, 12.24s/it] 8%|▊ | 821/10000 [2:59:37<31:07:27, 12.21s/it] 8%|▊ | 822/10000 [2:59:49<31:00:36, 12.16s/it] 8%|▊ | 823/10000 [3:00:04<32:42:41, 12.83s/it] 8%|▊ | 824/10000 [3:00:16<32:21:43, 12.70s/it] 8%|▊ | 825/10000 [3:00:28<32:12:18, 12.64s/it] 8%|▊ | 825/10000 [3:00:29<32:12:18, 12.64s/it] 8%|▊ | 826/10000 [3:00:41<32:11:11, 12.63s/it] 8%|▊ | 827/10000 [3:00:53<31:54:18, 12.52s/it] 8%|▊ | 828/10000 [3:01:06<32:10:18, 12.63s/it] 8%|▊ | 829/10000 [3:01:19<32:23:17, 12.71s/it] 8%|▊ | 830/10000 [3:01:31<31:55:12, 12.53s/it] 8%|▊ | 831/10000 [3:01:44<32:04:52, 12.60s/it] 8%|▊ | 832/10000 [3:01:56<31:37:02, 12.42s/it] 8%|▊ | 833/10000 [3:02:08<31:07:31, 12.22s/it] 8%|▊ | 834/10000 [3:02:20<31:25:59, 12.35s/it] 8%|▊ | 835/10000 [3:02:31<30:14:02, 11.88s/it] 8%|▊ | 836/10000 [3:02:44<30:54:10, 12.14s/it] 8%|▊ | 837/10000 [3:02:56<30:36:00, 12.02s/it] 8%|▊ | 838/10000 [3:03:07<30:13:36, 11.88s/it] 8%|▊ | 839/10000 [3:03:19<29:58:26, 11.78s/it] 8%|▊ | 840/10000 [3:03:30<29:50:35, 11.73s/it] 8%|▊ | 841/10000 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>> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-16 19:03:28,693 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-16 19:03:28,693 >> Batch size = 32 [INFO|trainer_utils.py:689] 2022-12-16 19:03:31,706 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 10%|█ | 1000/10000 [3:48:29<31:19:36, 12.53s/it][INFO|trainer.py:2700] 2022-12-16 19:16:09,867 >> Saving model checkpoint to ./checkpoint-1000 [INFO|configuration_utils.py:447] 2022-12-16 19:16:09,868 >> Configuration saved in ./checkpoint-1000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 19:16:12,439 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 19:16:12,454 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-16 19:16:19,376 >> Feature extractor saved in ./preprocessor_config.json 10%|█ | 1001/10000 [3:49:11<624:35:07, 249.86s/it] 10%|█ | 1002/10000 [3:49:23<446:04:00, 178.47s/it] 10%|█ | 1003/10000 [3:49:36<321:25:18, 128.61s/it] 10%|█ | 1004/10000 [3:49:47<233:41:48, 93.52s/it] 10%|█ | 1005/10000 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evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 15%|█▌ | 1500/10000 [5:42:39<29:15:19, 12.39s/it] 15%|█▌ | 1501/10000 [5:42:51<574:24:00, 243.30s/it] 15%|█▌ | 1502/10000 [5:43:04<410:51:34, 174.05s/it] 15%|█▌ | 1503/10000 [5:43:17<296:43:29, 125.72s/it] 15%|█▌ | 1504/10000 [5:43:29<216:02:38, 91.54s/it] 15%|█▌ | 1505/10000 [5:43:41<159:41:24, 67.67s/it] 15%|█▌ | 1506/10000 [5:43:52<119:57:19, 50.84s/it] 15%|█▌ | 1507/10000 [5:44:06<93:32:22, 39.65s/it] 15%|█▌ | 1508/10000 [5:44:18<74:08:22, 31.43s/it] 15%|█▌ | 1509/10000 [5:44:30<60:23:20, 25.60s/it] 15%|█▌ | 1510/10000 [5:44:42<50:49:31, 21.55s/it] 15%|█▌ | 1511/10000 [5:44:54<44:00:50, 18.67s/it] 15%|█▌ | 1512/10000 [5:45:06<39:09:50, 16.61s/it] 15%|█▌ | 1513/10000 [5:45:18<35:57:57, 15.26s/it] 15%|█▌ | 1514/10000 [5:45:31<34:05:55, 14.47s/it] 15%|█▌ | 1515/10000 [5:45:43<32:52:19, 13.95s/it] 15%|█▌ | 1516/10000 [5:45:56<31:44:15, 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If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 20%|██ | 2000/10000 [7:39:05<26:44:29, 12.03s/it][INFO|trainer.py:2700] 2022-12-16 23:06:45,865 >> Saving model checkpoint to ./checkpoint-2000 [INFO|configuration_utils.py:447] 2022-12-16 23:06:45,866 >> Configuration saved in ./checkpoint-2000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 23:06:48,466 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 23:06:48,481 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-16 23:06:58,809 >> Feature extractor saved in ./preprocessor_config.json 20%|██ | 2001/10000 [7:40:58<679:06:06, 305.63s/it] 20%|██ | 2002/10000 [7:41:09<483:01:10, 217.41s/it] 20%|██ | 2003/10000 [7:41:21<346:11:42, 155.85s/it] 20%|██ | 2004/10000 [7:41:34<250:29:15, 112.78s/it] 20%|██ | 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If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 25%|██▌ | 2500/10000 [9:34:25<25:28:13, 12.23s/it] 25%|██▌ | 2501/10000 [9:34:37<504:52:02, 242.37s/it] 25%|██▌ | 2502/10000 [9:34:49<360:36:44, 173.14s/it] 25%|██▌ | 2503/10000 [9:35:01<259:58:56, 124.84s/it] 25%|██▌ | 2504/10000 [9:35:13<189:20:58, 90.94s/it] 25%|██▌ | 2505/10000 [9:35:24<139:57:03, 67.22s/it] 25%|██▌ | 2506/10000 [9:35:36<105:21:22, 50.61s/it] 25%|██▌ | 2507/10000 [9:35:47<80:33:09, 38.70s/it] 25%|██▌ | 2508/10000 [9:36:00<64:16:55, 30.89s/it] 25%|██▌ | 2509/10000 [9:36:12<52:35:30, 25.27s/it] 25%|██▌ | 2510/10000 [9:36:23<43:45:56, 21.04s/it] 25%|██▌ | 2511/10000 [9:36:36<38:23:42, 18.46s/it] 25%|██▌ | 2512/10000 [9:36:47<34:05:09, 16.39s/it] 25%|██▌ | 2513/10000 [9:36:58<30:51:22, 14.84s/it] 25%|██▌ | 2514/10000 [9:37:11<29:20:57, 14.11s/it] 25%|██▌ | 2515/10000 [9:37:23<27:52:15, 13.40s/it] 25%|██▌ | 2516/10000 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and have been ignored: input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 30%|███ | 3000/10000 [11:28:28<24:43:49, 12.72s/it][INFO|trainer.py:2700] 2022-12-17 02:56:09,196 >> Saving model checkpoint to ./checkpoint-3000 [INFO|configuration_utils.py:447] 2022-12-17 02:56:09,198 >> Configuration saved in ./checkpoint-3000/config.json [INFO|modeling_utils.py:1680] 2022-12-17 02:56:12,512 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-17 02:56:12,527 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-17 02:56:24,248 >> Feature extractor saved in ./preprocessor_config.json 30%|███ | 3001/10000 [11:30:47<545:32:13, 280.60s/it] 30%|███ | 3002/10000 [11:31:00<389:22:29, 200.31s/it] 30%|███ | 3003/10000 [11:31:13<280:09:41, 144.14s/it] 30%|███ | 3004/10000 [11:31:25<203:15:14, 104.59s/it] 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Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-17 06:34:36,708 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-17 06:34:36,708 >> Batch size = 32 [INFO|trainer_utils.py:689] 2022-12-17 06:34:39,330 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 40%|████ | 4000/10000 [15:21:41<19:59:15, 11.99s/it][INFO|trainer.py:2700] 2022-12-17 06:49:21,663 >> Saving model checkpoint to ./checkpoint-4000 [INFO|configuration_utils.py:447] 2022-12-17 06:49:21,664 >> Configuration saved in ./checkpoint-4000/config.json [INFO|modeling_utils.py:1680] 2022-12-17 06:49:25,087 >> Model weights saved in ./checkpoint-4000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-17 06:49:25,105 >> Feature extractor saved in ./checkpoint-4000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-17 06:49:36,904 >> Feature extractor saved in ./preprocessor_config.json 40%|████ | 4001/10000 [15:24:00<526:09:09, 315.74s/it] 40%|████ | 4002/10000 [15:24:15<375:29:59, 225.38s/it] 40%|████ | 4003/10000 [15:24:28<269:07:31, 161.56s/it] 40%|████ | 4004/10000 [15:24:39<194:19:55, 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12.57s/it][INFO|trainer.py:2955] 2022-12-17 08:33:17,029 >> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-17 08:33:17,029 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-17 08:33:17,029 >> Batch size = 32 [INFO|trainer_utils.py:689] 2022-12-17 08:33:19,896 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. 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input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 50%|█████ | 5000/10000 [19:13:26<17:06:14, 12.31s/it][INFO|trainer.py:2700] 2022-12-17 10:41:07,072 >> Saving model checkpoint to ./checkpoint-5000 [INFO|configuration_utils.py:447] 2022-12-17 10:41:07,073 >> Configuration saved in ./checkpoint-5000/config.json [INFO|modeling_utils.py:1680] 2022-12-17 10:41:10,433 >> Model weights saved in ./checkpoint-5000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-17 10:41:10,451 >> Feature extractor saved in ./checkpoint-5000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-17 10:41:22,801 >> Feature extractor saved in ./preprocessor_config.json 50%|█████ | 5001/10000 [19:15:49<395:58:02, 285.15s/it] 50%|█████ | 5002/10000 [19:16:02<282:23:39, 203.41s/it] 50%|█████ | 5003/10000 [19:16:13<202:27:43, 145.86s/it] 50%|█████ | 5004/10000 [19:16:26<147:02:12, 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evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. 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[22:56:40<13:10:09, 11.85s/it] 60%|██████ | 6000/10000 [22:56:40<13:10:09, 11.85s/it][INFO|trainer.py:2955] 2022-12-17 14:24:21,128 >> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-17 14:24:21,128 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-17 14:24:21,128 >> Batch size = 32 [INFO|trainer_utils.py:689] 2022-12-17 14:24:23,602 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 60%|██████ | 6000/10000 [23:09:24<13:10:09, 11.85s/it][INFO|trainer.py:2700] 2022-12-17 14:37:05,087 >> Saving model checkpoint to ./checkpoint-6000 [INFO|configuration_utils.py:447] 2022-12-17 14:37:05,087 >> Configuration saved in ./checkpoint-6000/config.json [INFO|modeling_utils.py:1680] 2022-12-17 14:37:07,660 >> Model weights saved in ./checkpoint-6000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-17 14:37:07,675 >> Feature extractor saved in ./checkpoint-6000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-17 14:37:17,828 >> Feature extractor saved in ./preprocessor_config.json 60%|██████ | 6001/10000 [23:11:22<303:12:15, 272.95s/it] 60%|██████ | 6002/10000 [23:11:34<215:56:15, 194.44s/it] 60%|██████ | 6003/10000 [23:11:46<155:07:30, 139.72s/it] 60%|██████ | 6004/10000 [23:11:57<112:09:26, 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Evaluation ***** [INFO|trainer.py:2959] 2022-12-17 16:21:53,610 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-17 16:21:53,610 >> Batch size = 32 [INFO|trainer_utils.py:689] 2022-12-17 16:21:56,179 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length, speaker_id, duration, gender, age. 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If input_length, speaker_id, duration, gender, age are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 70%|███████ | 7000/10000 [27:02:37<11:01:56, 13.24s/it][INFO|trainer.py:2700] 2022-12-17 18:30:17,371 >> Saving model checkpoint to ./checkpoint-7000 [INFO|configuration_utils.py:447] 2022-12-17 18:30:17,372 >> Configuration saved in ./checkpoint-7000/config.json [INFO|modeling_utils.py:1680] 2022-12-17 18:30:20,689 >> Model weights saved in ./checkpoint-7000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-17 18:30:20,708 >> Feature extractor saved in ./checkpoint-7000/preprocessor_config.json [INFO|feature_extraction_utils.py:368] 2022-12-17 18:30:33,232 >> Feature extractor saved in ./preprocessor_config.json