{"id":18439563,"url":"https://github.com/idiap/exvo-2022","last_synced_at":"2026-02-27T09:16:25.081Z","repository":{"id":144961989,"uuid":"515561173","full_name":"idiap/ExVo-2022","owner":"idiap","description":"Extracting pre-trained self-supervised embeddings for ICML ExVO 2022 challenge","archived":false,"fork":false,"pushed_at":"2022-07-19T11:45:21.000Z","size":13,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-06-04T22:46:46.870Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/idiap.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSES/GPL-3.0-only.txt","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":"2022-07-19T11:44:40.000Z","updated_at":"2024-04-17T09:46:52.000Z","dependencies_parsed_at":null,"dependency_job_id":"026a75e9-f93b-485b-bb78-39d2eaabf5e1","html_url":"https://github.com/idiap/ExVo-2022","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/idiap/ExVo-2022","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idiap%2FExVo-2022","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idiap%2FExVo-2022/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idiap%2FExVo-2022/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idiap%2FExVo-2022/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/idiap","download_url":"https://codeload.github.com/idiap/ExVo-2022/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idiap%2FExVo-2022/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29889150,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-27T08:34:21.514Z","status":"ssl_error","status_checked_at":"2026-02-27T08:32:38.035Z","response_time":57,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":[],"created_at":"2024-11-06T06:25:27.239Z","updated_at":"2026-02-27T09:16:25.051Z","avatar_url":"https://github.com/idiap.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- SPDX-FileCopyrightText: Copyright © 2022 Idiap Research Institute \u003ccontact@idiap.ch\u003e\n\nSPDX-FileContributor: Bogdan Vlasenko \u003cbogdan.vlasenko@idiap.ch\u003e\n\nSPDX-License-Identifier: GPL-3.0-only --\u003e\n\n# Extracting pre-trained self-supervised embeddings for ICML ExVO 2022 challenge\n\nThis python code generates Generation SSL (WavLM) feature representation:\n\n- Estimate frame-level SSL embeddings from pre-trained models\n- Generates fixed-length feature representation by using mean and standard deviation functionals\n\nThe extracted feature representations were used during ICML ExVo 2022  challenge, for more details see:\n\nT. Purohit, I. B. Mahmoud, B. Vlasenko, M. Magimai.-Doss. Comparing supervised and self-supervised embedding for ExVo Multi-Task\nlearning track, workshop ICML ExVo 2022\n\n## Install\n\nCreate a Conda environment with python 3.10 and activate it:\n\n```bash\nconda create -n env python=3.10\nconda activate env\n```\nInstall all required python packages\n\n```bash\npip install -r requirements.txt\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidiap%2Fexvo-2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fidiap%2Fexvo-2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidiap%2Fexvo-2022/lists"}