Once files have been ingested, they can be submitted to a workflow. Currently, only primary files can be submitted to workflows, but supplementary files will soon be able to be submitted. To use the Workflow Submissions feature, first search for the item(s) you wish to submit to a workflow. Only items that have been submitted to AMP via the Batch Ingest feature will appear in the search results. The search feature allows users to limit search results by media type (audio, video, or other). The search results appear as items, with individual files contained in the item visible via a dropdown menu on each item, with an "add file" button to the right of the filename. For convenience, each item has an "add all files" button that adds every file to the "Selected Files" box. Once files have been added, they can either be submitted directly to a workflow or saved as a bundle. Saving a grouping of files as a bundle can be very helpful when adding a large number of files at once, as they can all be submitted simultaneously.
To select a workflow, click on the "Select Workflow" dropdown menu, which will provide a list of available workflows running on Galaxy. Once the workflow has been selected, the "submit to workflow" button will enable, allowing you to submit the files. Once one or more files have been submitted, AMP will display a message telling you how many jobs were successfully submitted to the workflow, and how many failed. Additionally, if one or more files fail, the message will display detailed information about the file(s), including the collection name, item name, file ID, filename, and file label. If one or more files fail, please let AMPPD staff know, providing the information given by the error message.
AMP has a variety of workflows, including the following: Transcript-NER-HMGM, Transcript-NER-no Human MGM, NER HMGM for Corrected Transcripts, Scene Detection with Contact Sheets, Contact Sheets Only, Contact Sheets Based on CM's Choices, Facial Recognition, and Applause Detection. The primary difference between the first two is that the former has human intervention at several steps to improve performance/the quality of the final deliverables. These workflows achieve two primary goals: generating a transcript (whether the transcript is human-edited or not), and recognizing named entities (people, places, etc.) in said transcript. The third, related to the former two, skips the transcript steps entirely, going directly to the named entity recognition steps. Scene Detection with Contact Sheets creates a contact sheet of video content by first automatically detecting shots using a Python library called PySceneDetect, then taking a frame in the middle of each of the said shots and placing them in order in a contact sheet. Contact Sheets Only creates only a contact sheet, taking frames from the video according to an arbitrary time interval. This is being superseded by Contact Sheets Based on CM's Choices, which uses timing parameters specified by individual collection managers in the AMP pilot. Facial Recognition presently is only trained on former IU president Herman B. Wells, but is otherwise a straightforward facial recognition implementation using the dlib Python library. Applause Detection uses an in-house TensorFlow-based model for detecting applause in musical performances – the idea being that applause is a reliable sign of the boundaries between individual musical works.