metadata
license: apache-2.0
metrics:
- accuracy
- roc_auc
base_model:
- facebook/wav2vec2-base-960h
Music genre classification is a fundamental and versatile application in many various domains. Some possible use cases for music genre classification include:
- music recommendation systems;
- content organization and discovery;
- radio broadcasting and programming;
- music licensing and copyright management;
- music analysis and research;
- content tagging and metadata enrichment;
- audio identification and copyright protection;
- music production and creativity;
- healthcare and therapy;
- entertainment and gaming.
The model is trained based on publicly available dataset of labeled music data — GTZAN Dataset — that contains 1000 sample 30-second audio files evenly split among 10 genres:
- blues;
- classical;
- country;
- disco;
- hip-hop;
- jazz;
- metal;
- pop;
- reggae;
- rock.
The final code is available as a Kaggle notebook. See also my Medium article for more details.