| 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. |