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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 that you think is suitable for audio classification, and use the exact same dataset `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:

    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
- Hubert model documentation
- M-CTC-T model documentation
- Audio Spectrogram Transformer documentation
- Wav2Vec2 documentation

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.

Update on GitHub