training_args = TrainingArguments(
     output_dir="my_awesome_asr_mind_model",
     per_device_train_batch_size=8,
     gradient_accumulation_steps=2,
     learning_rate=1e-5,
     warmup_steps=500,
     max_steps=2000,
     gradient_checkpointing=True,
     fp16=True,
     group_by_length=True,
     evaluation_strategy="steps",
     per_device_eval_batch_size=8,
     save_steps=1000,
     eval_steps=1000,
     logging_steps=25,
     load_best_model_at_end=True,
     metric_for_best_model="wer",
     greater_is_better=False,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=encoded_minds["train"],
     eval_dataset=encoded_minds["test"],
     tokenizer=processor,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

For a more in-depth example of how to finetune a model for automatic speech recognition, take a look at this blog post for English ASR and this post for multilingual ASR.