https://github.com/mgoltzsche/essentia-container
Docker container to retrieve musical information from audio data using Essentia extractors
https://github.com/mgoltzsche/essentia-container
ai alpine-linux audio audio-analysis audio-classification auto-tagging bpm container container-image docker docker-image essentia extractors information-retrieval mood-analysis music-analysis music-classification
Last synced: about 2 months ago
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Docker container to retrieve musical information from audio data using Essentia extractors
- Host: GitHub
- URL: https://github.com/mgoltzsche/essentia-container
- Owner: mgoltzsche
- License: apache-2.0
- Created: 2023-12-10T05:18:52.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-03T01:52:45.000Z (about 1 year ago)
- Last Synced: 2025-03-24T10:01:25.716Z (2 months ago)
- Topics: ai, alpine-linux, audio, audio-analysis, audio-classification, auto-tagging, bpm, container, container-image, docker, docker-image, essentia, extractors, information-retrieval, mood-analysis, music-analysis, music-classification
- Language: Dockerfile
- Homepage:
- Size: 9.77 KB
- Stars: 7
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# essentia-container
An alpine-based [Essentia](https://essentia.upf.edu/) extractor docker container image.
Essentia extractors allow to extract low and high level musical information from audio data (e.g. `average_loudness`, `bpm`, `danceability`, `electronic`).
Some of the extracted fields and detection accurracies are described [here](https://essentia.upf.edu/svm_models/accuracies_v2.1_beta1.html) (for an older version, though).
Differences compared to the [official pre-built binaries](https://essentia.upf.edu/extractors/) and [container](https://github.com/MTG/essentia-docker) (as of 2023):
* The binaries are compiled with [Gaia](https://github.com/MTG/gaia).
* Contains the [SVM models](https://essentia.upf.edu/svm_models/) and a corresponding [example profile](./profile.yaml).
* Uses Alpine Linux base image instead of Debian.
* Multi-arch image that works on both amd64 and arm64 Linux machines.
## Usage
The following example shows how to download an audio file, analyze it using [`essentia_streaming_extractor_music`](https://essentia.upf.edu/streaming_extractor_music.html) and print the result JSON:
```sh
docker run --rm ghcr.io/mgoltzsche/essentia sh -euxc '
URL=https://www.learningcontainer.com/wp-content/uploads/2020/02/Kalimba.mp3
wget -qO input.mp3 $URL
essentia_streaming_extractor_music input.mp3 - /etc/essentia/profile.yaml'
```
For more information, see the [Essentia Extractor documentation](https://essentia.upf.edu/extractors_out_of_box.html#extractors).
## Credits
Essentia is an open-source C++ library with Python bindings for audio analysis and audio-based music information retrieval.
It is released under the Affero GPLv3 license and is also available under proprietary license upon request.
This project is just a redistribution of the library, its extractor CLIs as well as its SVM models as a Linux container image.
[Learn more about the Essentia project](http://essentia.upf.edu)