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Including Contemporaneous Info in my YouTube Workflow

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From the Department of Wordy Titles

I have a set of tools that I have written to make interacting YouTube simpler, more straightforward, simplifying my workflow.

In the state it’s in it roughly looks like:

  1. record a bunch of videos
  2. upload the files and leave them in place
  3. run genjson on them to create a JSON template, including a reasonably-spaced publish schedule
  4. run get_ids to associate the JSON entries with the video’s YT videoId
  5. go through the videos, rewatch to decide on title, description and thumbnail frame and include this in the JSON entry
  6. run uploadytfootage to update the metadata

Most of the above is highly automated- even step 2 could be done away with if the default YouTube API quota didn’t limit one to roughly six videos per day.

The most labour-intensive part of the process is step 5. Because of the batch nature of the job, sometimes quite a few videos can pile up. For example, at time of writing I have 45 Hunt: Showdown videos from the past ten days to do.

Getting a short, catchy yet descriptive title and description for each of those will involve reacquainting myself with what those round[s] entailed. So I decided recently that I would try to do some of that work as I go: between rounds of Hunt, write out a putative title and description associated with a video file to another JSON file.

I also capture a short snippet or potential title on a notepad on my desk:

Between those hopefully the process will be a bit easier.

I also cooked up a short script to merge together the two JSON files. The crux of it is the filter that selects from the ‘contemporaneous note’ if it has an associated entry for a file in the generated JSON template list.

We are working with a list of dicts, so a list comprehension is handy. We want to select from the list of dicts an entire dict that matches the filename of the video. Roughly speaking:

next(item for item in json_c if item["file"] = filename)

Docs: list comprehension, next()
SO example: Python list of dictionaries search

If I am able to keep on top of titles and descriptions as I go, the only thing needed will be to find a good thumbnail frame! (though that’s kinda time consuming in itself, perhaps ML could be applied to that…)

Edit: Yes! Deep neural net thumbnails and convolutional neural nets (PDF)

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