 Usage  The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.  [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/audio-classification/run_audio_classification.py) is an audio classification example script to fine-tune a model. You can run it with Wav2Vec2 with the following command: ```bash python run_audio_classification.py \     --model_name_or_path facebook/wav2vec2-base \     --dataset_name superb \     --dataset_config_name ks \     --output_dir /tmp/wav2vec2-base-ft-keyword-spotting \     --overwrite_output_dir \     --remove_unused_columns False \     --do_train \     --do_eval \     --learning_rate 3e-5 \     --max_length_seconds 1 \     --attention_mask False \     --warmup_ratio 0.1 \     --num_train_epochs 5 \     --per_device_train_batch_size 256 \     --per_device_eval_batch_size 256 \     --dataloader_num_workers 4 \     --seed 27 \     --use_habana \     --use_lazy_mode \     --gaudi_config_name Habana/wav2vec2 \     --throughput_warmup_steps 2 ```  Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. 