effnet-discogs
Maintainer: mtg
114
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No 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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