error with ''from transformers import Seq2SeqTrainingArguments''

[freeexit2002]

I would greatly appreciate your help with this error. Here is [tutorial] (https://huggingface.co/blog/fine-tune-whisper) that i followed. Thanks in advance.

''from transformers import Seq2SeqTrainingArguments

training_args = Seq2SeqTrainingArguments( output_dir="./whisper-large-v2-el", # change to a repo name of your choice per_device_train_batch_size=16, gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size learning_rate=1e-5, warmup_steps=500, max_steps=4000, gradient_checkpointing=True, fp16=True, evaluation_strategy="steps", per_device_eval_batch_size=8, predict_with_generate=True, generation_max_length=225, save_steps=1000, eval_steps=1000, logging_steps=25, report_to=["tensorboard"], load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, )

---

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ImportError Traceback (most recent call last)

in <cell line: 3>() 1 from transformers import Seq2SeqTrainingArguments 2 ----> 3 training_args = Seq2SeqTrainingArguments( 4 output_dir="./whisper-small-hi", # change to a repo name of your choice 5 per_device_train_batch_size=16,

4 frames

/usr/local/lib/python3.9/dist-packages/transformers/training_args_seq2seq.py in init(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, xpu_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, sharded_ddp, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, dataloader_pin_memory, skip_mem...

/usr/local/lib/python3.9/dist-packages/transformers/training_args.py in post_init(self) 1253 self.framework == "pt" 1254 and is_torch_available() -> 1255 and (self.device.type != "cuda") 1256 and (get_xla_device_type(self.device) != "GPU") 1257 and (self.fp16 or self.fp16_full_eval)

/usr/local/lib/python3.9/dist-packages/transformers/training_args.py in device(self) 1631 """ 1632 requires_backends(self, ["torch"]) -> 1633 return self._setup_devices 1634 1635 @property

/usr/local/lib/python3.9/dist-packages/transformers/utils/generic.py in get(self, obj, objtype) 52 cached = getattr(obj, attr, None) 53 if cached is None: ---> 54 cached = self.fget(obj) 55 setattr(obj, attr, cached) 56 return cached

/usr/local/lib/python3.9/dist-packages/transformers/training_args.py in _setup_devices(self) 1533 logger.info("PyTorch: setting up devices") 1534 if not is_sagemaker_mp_enabled() and not is_accelerate_available(check_partial_state=True): -> 1535 raise ImportError( 1536 "Using the Trainer with PyTorch requires accelerate: Run pip install --upgrade accelerate" 1537 )

ImportError: Using the Trainer with PyTorch requires accelerate: Run pip install --upgrade accelerate

---

NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the "Open Examples" button below.

[bbietzsche]

firstly

!pip uninstall accelerate !pip install accelerate==23.0

after that you have to restart runtime.