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Add info about requirements #1
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Also dependencies could be simplified. Considering that ideally all audio analysis tools of Audio Commons could be unified in a single tool, it would be good that they had similar dependencies. In https://github.com/AudioCommons/ac-audio-extractor he have included a single audio extractor for musical properties of music samples which in the future could be extended to incorporate all annotation tools. In this repo we provide a Docker image to simplify the problems with dependencies. This docker image already has Essentia bundled in it (and the python bindings). A way to simplify the current timbral models would be to use Nevertheless it should be first tested if the obtained results are the same (or very similar) after these change of dependencies. Audio files can be loaded in essentia using one of its loaders, for example:
Onsets can be computed using the |
I've added text in the README about dependencies and how they can be installed. In a future update, the code will be restructured so that it can be downloaded with I will also look into replacing the librosa library with essentia after the deliverable is finished; however, I'm concerned that since there isn't a pip install version, this may make the dissemination harder. |
Some of the scripts require external software to be installed and won't work out of the box (e.g.
numpy
,soundfile
,librosa
). These should al least be listed in theREADME
. In future iterations it would be great to provide a docker image (we can do it as part of the main audio commons extractor) to facilitate running these algorithms.The text was updated successfully, but these errors were encountered: