{"id":18733924,"url":"https://github.com/mgoltzsche/essentia-container","last_synced_at":"2025-04-10T11:10:56.211Z","repository":{"id":211680526,"uuid":"729712355","full_name":"mgoltzsche/essentia-container","owner":"mgoltzsche","description":"Docker container to retrieve musical information from audio data using Essentia extractors","archived":false,"fork":false,"pushed_at":"2024-04-03T01:52:45.000Z","size":10,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-24T10:01:25.716Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"","language":"Dockerfile","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mgoltzsche.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-12-10T05:18:52.000Z","updated_at":"2025-03-24T00:10:20.000Z","dependencies_parsed_at":"2024-11-07T15:11:57.494Z","dependency_job_id":"b61ebcab-3679-4d3b-ac6c-e698eb5a6707","html_url":"https://github.com/mgoltzsche/essentia-container","commit_stats":null,"previous_names":["mgoltzsche/essentia-container"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgoltzsche%2Fessentia-container","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgoltzsche%2Fessentia-container/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgoltzsche%2Fessentia-container/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mgoltzsche%2Fessentia-container/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mgoltzsche","download_url":"https://codeload.github.com/mgoltzsche/essentia-container/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248208561,"owners_count":21065202,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-11-07T15:11:48.477Z","updated_at":"2025-04-10T11:10:56.188Z","avatar_url":"https://github.com/mgoltzsche.png","language":"Dockerfile","funding_links":[],"categories":[],"sub_categories":[],"readme":"# essentia-container\n\nAn alpine-based [Essentia](https://essentia.upf.edu/) extractor docker container image.\n\nEssentia extractors allow to extract low and high level musical information from audio data (e.g. `average_loudness`, `bpm`, `danceability`, `electronic`).\nSome 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).\n\nDifferences compared to the [official pre-built binaries](https://essentia.upf.edu/extractors/) and [container](https://github.com/MTG/essentia-docker) (as of 2023):\n* The binaries are compiled with [Gaia](https://github.com/MTG/gaia).\n* Contains the [SVM models](https://essentia.upf.edu/svm_models/) and a corresponding [example profile](./profile.yaml).\n* Uses Alpine Linux base image instead of Debian.\n* Multi-arch image that works on both amd64 and arm64 Linux machines.\n\n## Usage\n\nThe 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:\n```sh\ndocker run --rm ghcr.io/mgoltzsche/essentia sh -euxc '\n  URL=https://www.learningcontainer.com/wp-content/uploads/2020/02/Kalimba.mp3\n  wget -qO input.mp3 $URL\n  essentia_streaming_extractor_music input.mp3 - /etc/essentia/profile.yaml'\n```\n\nFor more information, see the [Essentia Extractor documentation](https://essentia.upf.edu/extractors_out_of_box.html#extractors).\n\n## Credits\n\nEssentia is an open-source C++ library with Python bindings for audio analysis and audio-based music information retrieval.\nIt is released under the Affero GPLv3 license and is also available under proprietary license upon request.\nThis project is just a redistribution of the library, its extractor CLIs as well as its SVM models as a Linux container image.\n[Learn more about the Essentia project](http://essentia.upf.edu)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmgoltzsche%2Fessentia-container","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmgoltzsche%2Fessentia-container","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmgoltzsche%2Fessentia-container/lists"}