# Tv Archive AI pipeline {% embed url="" %} ## Notes * SLIDES: * Internet Archive! * Petabytes of data! The TV Archive! The TV News Archive! Recording TV, cable news, Philadelphia local news, CSPAN (people LOVE it). * There's an API written in Python Clip-based metadata storage. "This clip was fact-checked, this clip is a politlcal ad" Check it out: What is in the data? VIDEO / AUDIO / CAPTIONS / but also information about… PROGRAM / CONTEXT / AUDIENCE Not all of this data is easy to grab but each have signals where data can be pulled from. * \[ see excellent slide with detailed examples for each ] * How can we use these signals to provide more information? * Speaker diarization / who is talking? * Experimentation at the Internet Archive: * They want to enable experimentation as a library, not necessarily do it (but they do it too) * Example: Political TV Ad Archive () * Frequency analysis, turning sound into a hash and easily searchable. FIngerprinted each political ad and found all other copies with that ad. * Example: Face o matic * Recording in real time, 4 hours to convert into mp4 and other things, run through an algorithm that "doesn't take much time at all" * This is a prototype: What do YOU want to use it for? - What format would you like this in? * Example output in #face-o-matic on hyperaudio slack * Example: Chyron Extraction Extracting the "lower thirds" and how do they compare? That will be discussed tomorrow\* * The Vision: What's happening on TV? What's all the metadata associated with what's happening? They don't have a great API for these things but want to use Sockets to push things out. * Be able to look historically, last month, etc. INTERNET ARCHIVE IS A LIBRARY! Dan wants to make sure you know this. ← it’s true. * Question: * Can you add more data to the archive? How can submitters use all this cool stuff? * "Good question." * "Reach out to me, basically." → DAN @slifty SCHULTZ * "We should talk" everyone talk to him! --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://textav.gitbook.io/textav-event/problem-domains/tv-archive-ai-pipeline.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.