# Hands-on exercise

It's time to get your hands on some Audio models and apply what you have learned so far. 
This exercise is one of the four hands-on exercises required to qualify for a course completion certificate.

Here are the instructions.
In this unit, we demonstrated how to fine-tune a Hubert model on `marsyas/gtzan` dataset for music classification. Our example achieved 83% accuracy. 
Your task is to improve upon this accuracy metric. 

Feel free to choose any model on the [🤗 Hub](https://huggingface.co/models) that you think is suitable for audio classification, 
and use the exact same dataset [`marsyas/gtzan`](https://huggingface.co/datasets/marsyas/gtzan) to build your own classifier.

Your goal is to achieve 87% accuracy on this dataset with your classifier. You can choose the exact same model, and play with the training hyperparameters, 
or pick an entirely different model - it's up to you!   

For your result to count towards your certificate, don't forget to push your model to Hub as was shown in this unit with 
the following `**kwargs` at the end of the training:

```python
kwargs = {
    "dataset_tags": "marsyas/gtzan",
    "dataset": "GTZAN",
    "model_name": f"{model_name}-finetuned-gtzan",
    "finetuned_from": model_id,
    "tasks": "audio-classification",
}

trainer.push_to_hub(**kwargs)
```

Here are some additional resources that you may find helpful when working on this exercise:
* [Audio classification task guide in Transformers documentation](https://huggingface.co/docs/transformers/tasks/audio_classification)
* [Hubert model documentation](https://huggingface.co/docs/transformers/model_doc/hubert)
* [M-CTC-T model documentation](https://huggingface.co/docs/transformers/model_doc/mctct)
* [Audio Spectrogram Transformer documentation](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)
* [Wav2Vec2 documentation](https://huggingface.co/docs/transformers/model_doc/wav2vec2)

Feel free to build a demo of your model, and share it on Discord! If you have questions, post them in the #audio-study-group channel.


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