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--- |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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- roc_auc |
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base_model: |
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- facebook/wav2vec2-base-960h |
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--- |
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[Music genre](https://en.wikipedia.org/wiki/Music_genre) classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include: |
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- music recommendation systems; |
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- content organization and discovery; |
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- radio broadcasting and programming; |
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- music licensing and copyright management; |
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- music analysis and research; |
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- content tagging and metadata enrichment; |
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- audio identification and copyright protection; |
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- music production and creativity; |
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- healthcare and therapy; |
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- entertainment and gaming. |
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The model is trained based on publicly available dataset of labeled music data — [GTZAN Dataset](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification) — that contains 1000 sample 30-second audio files evenly split among 10 genres: |
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- blues; |
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- classical; |
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- country; |
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- disco; |
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- hip-hop; |
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- jazz; |
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- metal; |
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- pop; |
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- reggae; |
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- rock. |
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The final code is available as a [Kaggle notebook](https://www.kaggle.com/code/dima806/music-genre-classification-wav2vec2-base-960h). |
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See also [my Medium article](https://medium.com/data-and-beyond/building-a-free-advanced-music-genre-classification-pipeline-using-machine-learning-654b0de7cc3e) for more details. |
