| Instead, we'll | |
| only look at the loss: | |
| thon | |
| from transformers import Seq2SeqTrainingArguments | |
| training_args = Seq2SeqTrainingArguments( | |
| output_dir="speecht5_finetuned_voxpopuli_nl", # change to a repo name of your choice | |
| per_device_train_batch_size=4, | |
| gradient_accumulation_steps=8, | |
| learning_rate=1e-5, | |
| warmup_steps=500, | |
| max_steps=4000, | |
| gradient_checkpointing=True, | |
| fp16=True, | |
| evaluation_strategy="steps", | |
| per_device_eval_batch_size=2, | |
| save_steps=1000, | |
| eval_steps=1000, | |
| logging_steps=25, | |
| report_to=["tensorboard"], | |
| load_best_model_at_end=True, | |
| greater_is_better=False, | |
| label_names=["labels"], | |
| push_to_hub=True, | |
| ) | |
| Instantiate the Trainer object and pass the model, dataset, and data collator to it. |