mtg / music-classifiers

Transfer learning models for music classification by genres, moods, and instrumentation

  • Public
  • 10K runs
  • T4
  • GitHub
  • Paper
  • License
Run with an API
Playground API Examples README Versions

Input

Current Time 00:00:000
Duration 00:00:000
Loaded: 0%
Stream Type LIVE
Remaining Time 00:00:000
 
1x
file

Audio file to process

string
Shift + Return to add a new line

YouTube URL to process (overrides audio input)

Default: ""

string

Model type (embeddings)

Default: "effnet-discogs"

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Output

Rendering markdown...

Generated in
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This example was created by a different version, mtg/music-classifiers:1ecc5a7d.

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This demo runs transfer learning classifiers trained on various public and in-house MTG datasets using different audio embeddings.

Source models used for embeddings

  • MusiCNN. A musically motivated CNN with two variants trained on the Million Song Dataset and the MagnaTagATune.
  • VGGish. A large VGG variant trained on a preliminary version of the AudioSet Dataset.

Transfer learning classifiers

Our models consist of single-hidden-layer MLPs trained on the considered embeddings.

License

These models are part of Essentia Models made by MTG-UPF and are publicly available under CC by-nc-sa and commercial license.