![]() The second extractor uses prebuilt trained models for prediction. Support ( -with-gaia) otherwise will not be able to use the high level models. Is somewhat complex but with some cross searching on the internet you will make it. You will also need the streaming_extractor_music binary extractor from the Essentia project. The plugin can be installed via: $ pip install beets-xtractorĪnd activated the usual way by adding xtractor to the list of plugins in your configuration: plugins : - xtractor Install the Essentia extractors Mood_mirex_cluster_1, mood_mirex_cluster_2, mood_mirex_cluster_3, mood_mirex_cluster_4, mood_mirex_cluster_5 Installation Mood_aggressive, mood_electronic, mood_happy, mood_sad, mood_party, mood_relaxed, mood_mirex, ![]() Genre_rosamerica, voice_instrumental, is_voice, is_instrumental, mood_acoustic, To obtain low and high level musical information from your songs.Ĭurrently, the following attributes are extracted for each library item:īpm, danceability, beats_count, average_loudness, danceable, gender, is_male, is_female, The beets-xtractor plugin lets you, through the use of the Essentia extractors,
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