effnet-discogs

Maintainer: mtg

Total Score

114

Last updated 6/13/2024
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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

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Model overview

The effnet-discogs model is an EfficientNet model trained for music style classification. It can identify 400 different music styles from the Discogs taxonomy. This model is part of a collection of music classification models developed by the Music Technology Group (MTG) at Universitat Pompeu Fabra.

The effnet-discogs model is similar to other music classification models from MTG, such as music-classifiers, which can identify music genres, moods, and instrumentation, and music-approachability-engagement, which classifies music based on approachability and engagement.

Model inputs and outputs

The effnet-discogs model takes either a YouTube URL or an audio file as input, and outputs a list of the top N most likely music styles from the Discogs taxonomy, along with their probabilities.

Inputs

  • Url: A YouTube URL to process (overrides the audio input)
  • Audio: An audio file to process
  • Top N: The number of top music styles to return (default is 10)
  • Output Format: The output format, which can be either a bar chart visualization or a JSON blob

Outputs

  • Output: A URI (URL) pointing to the model's output, which can be either a visualization or a JSON blob

Capabilities

The effnet-discogs model can accurately classify music into 400 different styles from the Discogs taxonomy. This can be useful for music discovery, recommendation, and analysis applications.

What can I use it for?

You can use the effnet-discogs model to build music classification and discovery applications. For example, you could integrate it into a music streaming service to provide detailed genre and style information for the songs in your library. You could also use it to build a music discovery tool that recommends new artists and genres based on a user's listening history.

Things to try

Try using the effnet-discogs model to classify a diverse set of music, including both popular and obscure genres. This can help you understand the model's capabilities and limitations, and identify potential areas for improvement or fine-tuning.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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