| Name | audpsychometric JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Analyze and summarize human annotations |
| upload_time | 2024-09-05 14:36:36 |
| maintainer | None |
| docs_url | None |
| author | Sandrine Lefort |
| requires_python | None |
| license | MIT License Copyright (c) 2018-2022 audEERING GmbH and Contributors Authors: Christian Geng Sandrine Lefort Hagen Wierstorf Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
audio
data
dataset
annotation
mlops
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
===============
audpsychometric
===============
**audpsychometric** analyses rater annotations.
It allows to calculate averages,
confidences,
and agreements
across raters.
Raw data
{
"_id": null,
"home_page": null,
"name": "audpsychometric",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "audio, data, dataset, annotation, mlops, machine learning",
"author": "Sandrine Lefort",
"author_email": "Hagen Wierstorf <hwierstorf@audeering.com>, Christian Geng <cgeng@audeering.com>",
"download_url": "https://files.pythonhosted.org/packages/2d/ff/6dac550554feffa76154d7c8086cb269fba1d9082c0cd79c1a62e226f0e2/audpsychometric-0.1.1.tar.gz",
"platform": null,
"description": "===============\naudpsychometric\n===============\n\n**audpsychometric** analyses rater annotations.\n\nIt allows to calculate averages,\nconfidences,\nand agreements\nacross raters.\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2018-2022 audEERING GmbH and Contributors Authors: Christian Geng Sandrine Lefort Hagen Wierstorf Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
"summary": "Analyze and summarize human annotations",
"version": "0.1.1",
"project_urls": {
"documentation": "https://audeering.github.io/audpsychometric/",
"repository": "https://github.com/audeering/audpsychometric/"
},
"split_keywords": [
"audio",
" data",
" dataset",
" annotation",
" mlops",
" machine learning"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "9781700c0853d5287e0da7eca9f533d722abbcf3de9b5e915ef9437568f740d6",
"md5": "caef91fc9f21e4cd02a673f035ea0646",
"sha256": "0b2215cbe2c97d09725ae8a19f0b6034f24ba8deb83a015403c03230fd8b76ea"
},
"downloads": -1,
"filename": "audpsychometric-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "caef91fc9f21e4cd02a673f035ea0646",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 30515,
"upload_time": "2024-09-05T14:36:35",
"upload_time_iso_8601": "2024-09-05T14:36:35.213666Z",
"url": "https://files.pythonhosted.org/packages/97/81/700c0853d5287e0da7eca9f533d722abbcf3de9b5e915ef9437568f740d6/audpsychometric-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2dff6dac550554feffa76154d7c8086cb269fba1d9082c0cd79c1a62e226f0e2",
"md5": "353ecd33c68672dfe0e66b378ce2f090",
"sha256": "e452690cfef1dd484735d43f6187e2a536d017c0262826356c0454fd72e5eaba"
},
"downloads": -1,
"filename": "audpsychometric-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "353ecd33c68672dfe0e66b378ce2f090",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 31150,
"upload_time": "2024-09-05T14:36:36",
"upload_time_iso_8601": "2024-09-05T14:36:36.561022Z",
"url": "https://files.pythonhosted.org/packages/2d/ff/6dac550554feffa76154d7c8086cb269fba1d9082c0cd79c1a62e226f0e2/audpsychometric-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-05 14:36:36",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "audeering",
"github_project": "audpsychometric",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "audpsychometric"
}
