51–60 of 177 posts
I emailed you but did something similar and posted to Show HN a while back - https://weekend-collection.s3.amazonaws.com/Catalog+-+Feb+17...
https://weekendcollection.substack.com/p/technical-details
(Recursive coarse clustering as opposed to one-shot fine clustering.)
Nice project OP. I also love In Our Time. Some favourite episodes off the top of my head: * Wilfred Owen - https://www.bbc.co.uk/programmes/m001df48 * The Evolution of Crocodiles - https://www.bbc.co.uk/programmes/m000zmhf * The May Forth Movement - https://www.bbc.co.uk/programmes/m001282c * The Valladolid Debate - https://www.bbc.co.uk/programmes/m000fgmw * Gerard Manley Hopkins - https://www.bbc.co.uk/programmes/m…
- The evolution of teeth https://www.bbc.co.uk/programmes/m0003zbg
- The fish-tetrapod transition https://www.bbc.co.uk/programmes/m001d56q
- The late Devonian extinction https://www.bbc.co.uk/programmes/m000sz7x
- The American West https://www.bbc.co.uk/programmes/p00548gg
- Metamorphosis (Ovid) https://www.bbc.co.uk/programmes/p00546p6
- Politeness https://www.bbc.co.uk/programmes/p004y29m
- The Bronze Age collapse https://www.bbc.co.uk/programmes/b07fl5bh
- Doggerland https://en.wikipedia.org/wiki/Doggerland
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I've been considering this, but my assumption is that it would be tripped up by the specialist words. I wonder... is there a way to "prime" Whisper (e.g. with the embedding of the episode synopsis) so that it "listens out" for words related to a particular topic? I haven't looking but this would be neat!
In my experience, Whisper does a great job even with specialized terminology. It won't catch everything, but I think it will exceed your expectations. One of the hardest things about Whisper is choosing which model to use; they offer a variety of sizes, and sometimes the smaller ones do better than the larger ones. It's worth trying a few different models and deciding what is best for each particular application. I w…
Update: turns out this exists already: https://platform.openai.com/docs/guides/speech-to-text/promp...
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Love the visual map. What does color mean? Any way to do a 3rd PC, and put the visualization in a cube one can toy around with?
Colour is the 3rd component -- I wanted to see the difference between overlapping episodes. As for the 3D plot... here you go! https://interconnected.org/more/2023/03/in_our_time-PCA-3D-p... Basic PCA + Plotly is actually in OpenAI's official Python library (in `embedding_utils`) -- this plot is just the output from that.
Seems like everyone has been getting excited around the search or code-generation use cases ... or simply trying to make it say naughty things (boring, not interested, wake up in a few more years), but this is eye opening.
The idea of this as a "universal coupler" is fascinating, and I think I agree with the author that we are probably standing at an early-90s-web moment with LLMs as a function call (the technology is kinda-there and mostly-works, and people are trying out a lot of ideas ... some work, some don't).
My mind is racing. Thanks for the epiphany moment.
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I've been considering this, but my assumption is that it would be tripped up by the specialist words. I wonder... is there a way to "prime" Whisper (e.g. with the embedding of the episode synopsis) so that it "listens out" for words related to a particular topic? I haven't looking but this would be neat!
I haven't tried, but the Open AI docs mention priming on the whisper model being available prompt string Optional An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.
Some unlinked features... If you put the Dewey division in the URL, the directory auto-opens. e.g. here are episodes about prehistoric life (my current jumping-off point) https://genmon.github.io/braggoscope/directory#560 There's a visual map of episodes. After principal component analysis of the episode embedding vectors, these are the most significant two components as the x,y https://genmon.github.io/braggoscope/m…
Some unlinked features... If you put the Dewey division in the URL, the directory auto-opens. e.g. here are episodes about prehistoric life (my current jumping-off point) https://genmon.github.io/braggoscope/directory#560 There's a visual map of episodes. After principal component analysis of the episode embedding vectors, these are the most significant two components as the x,y https://genmon.github.io/braggoscope/m…
Thinking out loud: Add the experts, not just the reading lists. They're a jumping-off point into academic-paper-space. What have they published? In what journals? Who have they collaborated with?
Earlier quoted context omitted.
In my experience, Whisper does a great job even with specialized terminology. It won't catch everything, but I think it will exceed your expectations. One of the hardest things about Whisper is choosing which model to use; they offer a variety of sizes, and sometimes the smaller ones do better than the larger ones. It's worth trying a few different models and deciding what is best for each particular application. I w…
I'd still like the ability to prime Whisper. I used it to transcribe a podcast episode I appeared on recently and one of the fixes I had to make was that ChatGPT came out as "chat GPT" every time it was mentioned: https://simonwillison.net/2023/Mar/7/kqed-forum/#kqed-forum Update: turns out this exists already: https://platform.openai.com/docs/guides/speech-to-text/promp...
This was the same audio sample fed through each of those four models, with no "prompting" as you're discussing.
If I add "--initial_prompt ChatGPT", then all four models are able to get the spelling correct.
Regardless, I don't think "chat GPT" versus "ChatGPT" is a huge deal. There will always be some level of uncertainty and ambiguity in the transcript, and even books written by humans always have a few typos get past multiple stages of copy editing. Perfection is virtually unachievable, but you can always scroll through the transcript and make some edits after the fact, if desired. Maybe some future model will magically eliminate all typos.
I went to https://genmon.github.io/braggoscope/guests and opened the Firefox DevTools console and ran this:
guests = Array.from(document.querySelectorAll('ul a.text-blue-500.underline')).map(el => ({
name: el.innerText, count: parseInt(el.nextElementSibling.textContent.slice(1, -1), 10)
}))
guests.sort((a, b) => a.count [
{
"name": "Simon Schaffer",
"count": 24
},
{
"name": "Angie Hobbs",
"count": 23
},
{
"name": "Martin Palmer",
"count": 22
},
{
"name": "Steve Jones",
"count": 21
},
{
"name": "Paul Cartledge",
"count": 20
}