Update dataset - 1761526984.218887

annotations/1.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:43f7686ad75c5c422854b093343d6e74cb8319464f174746d6ce54300c64b784
-size 602
+oid sha256:1ef573e41868438568b70cb756a76484f35624b9b1687ca691aa6fdd321fea83
+size 597

annotations/10.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a9ab828f6e034dcf45a63b667f029dddd2abbaece273e160e844360dc6de2b69
+oid sha256:e9f299d5208dc8f29b5720551de52fbc502b6e5214d2fca4e1cd4c65012d15bd
 size 599

annotations/11.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:990e161aed20ca2415a2c27d0e26374736ab35915276d3eaf488a2b054cb7a66
-size 603

annotations/12.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c0140b0e0ada41ef86c16dea4292fb23e80ec48c6a763d26d901ceaaad8bfdec
-size 604

annotations/13.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:428f06829a251840e1782e23e5abf989d76f3d2cc3ab412d7a1485be08380322
-size 596

annotations/14.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:11d120274146f06c627a1537fcc6bc8265d9a893fee315c93a0459f3bca6b026
-size 598

annotations/15.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c466b5621bae69c4359cb10483a01de397b75c29ad8504061ee9d3a74ee35d56
-size 598

annotations/16.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fbe28802e5c299348937553df13bf1e944d2a8a9632cf0aeba22311fa71446c8
-size 603

annotations/17.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cbdcc222aecc5f34b0cac109b479f6cc34740fa9cf336028f9affc363f8ce7ae
-size 602

annotations/18.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:956cc426f4fff9bd2ee833479d4d3c5bc1f93cd7f5a12e9cff92a93458f4ec60
-size 600

annotations/19.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7dd24e928b46e823225cf4316ca2b0fa72a857eadc56a80b5f43e8333f6f731c
-size 601

annotations/2.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a741c3bf11b3da2bccbe2f5cb194ea56a30d00880005051ba6ac81b9fd4c7c34
+oid sha256:6905bfed86dfec9c4b87c77eea691631a3f1fe81efec09692e06eca2489c32ed
 size 602

annotations/20.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:39d9f338f8b06232d5c8a69f64f2c8312994134382763f974a4ed63523b8601d
-size 598

annotations/21.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d563d3f5649310dd51a905048f33d346539321763c4d9a4d7d4c00ac7f31d633
-size 602

annotations/3.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:78227e0e5d69a67727184ef67bcb1792382ecfb15949a5d6a8d491d12ad2735d
+oid sha256:9f38dee5fd65537ec99c28177860f73b8ea944e20a84ed036263b6978dbce7be
 size 603

annotations/4.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ed70b40a41d226b3c322aa41ebd9d459a694bb53893c0861e600b0324f2b852b
-size 598
+oid sha256:cbe75f4d6a4d8dc4816b249f4ca538aaa878690db5cc0950a824bd9093bc7000
+size 596

annotations/5.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f8df26fe9575bdbbdb21f57b27c96e17b667f4d1e7b2504c32bcb56982a9e0d0
-size 601
+oid sha256:ff95dbe981ebb9e13133c021bff8e3442cc0168387c2d8d91c3a5d2819f54edd
+size 598

annotations/6.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f96c04e98cb031a98d3e87c297a22a65b826dfb4013d24342680fac0249b7af8
+oid sha256:6d80c741cd54ac37f0d1a5a88733f86e4ef511f88c56fd7db5647ec604177c5b
 size 598

annotations/7.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2b8ce5e4339e52cb704a14b55e716125178654662550c888c321f22c7d4f8fbd
-size 598
+oid sha256:61ce307495371bfcfadae60591dc9b3dd1d20a41540c1f88bbaf164264c4f5d1
+size 602

annotations/8.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2fa308b235032507248596e2c12c81e862a9fe37e77f1f3512ea5d1b0f467b30
-size 603
+oid sha256:b1c32219ebb9ecf47672ba4d7a1fc64b94e9d7f3d6f6c85b1c3982c47330894e
+size 600

annotations/9.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7f30b52f11aee7dc0d7b0d6bee87bce288798669353aaab33862bd13931eebce
-size 597
+oid sha256:76971585acea228e62831c3c086691f002d652aabdc2fdd36b0865fb4f6d896b
+size 601

audio/1.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e946b6ca2eb04be83bb174d3e358090f03e670c0a9133da4a06406a24d96da31
-size 2668076
+oid sha256:fcc72030e7b00e1fe1cc439adc827178a773a020a0a87e41077bc42ccf42126f
+size 2587436

audio/10.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1a692a5efca0e78afd528ef4edcff7d69e64cbb5989ae20f31ad01cf2faeb271
-size 2577644
+oid sha256:4abd166ccade3f644d84f0b394d7c64f90c5b8adee074b6b0bcfb53b95b8e07d
+size 2814956

audio/11.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:55a4246d9bf9bfdfc1b28b4add8b4a8c746b7473f10e28ff3f909709017b04eb
-size 2070764

audio/12.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4abd166ccade3f644d84f0b394d7c64f90c5b8adee074b6b0bcfb53b95b8e07d
-size 2814956

audio/13.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:706082dc880f42aa397f9aee429f2f8a4d62fa19417e106e646d6031f91e4f11
-size 7845164

audio/14.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:de35c8502abe369ee5876eea1d354a292d135675764ea7606e835a146c7b191c
-size 8816183

audio/15.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e831b32171884e6ac24dd23e3e54973da861f48f44a47a5e8fbecf2bc6720438
-size 4359902

audio/16.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e47a89f43f0de0b54b995ac790cf0041677f8944e208b009dc1caa6029fcf414
-size 888236

audio/17.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0fe09dc5bf67af9a6f92fcf38b02508567fb8ce34984e744908386add67de18f
-size 3113324

audio/18.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6a5c658f0ee134e0c31a9ca939ad7805a9a76a321f5e3728dd575ce734c250ae
-size 1484396

audio/19.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:73519e4c374f1b7aa73fafe009ab248ad470a0a17e9b522d265af6293a246021
-size 1006406

audio/2.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fcc72030e7b00e1fe1cc439adc827178a773a020a0a87e41077bc42ccf42126f
-size 2587436
+oid sha256:2ea80e81bd6d3a5fd0addc1a52eca7496af6a382ff83a711675817d274b92300
+size 3605804

audio/20.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a7ed9e2ba97d3231457b3e699f67130488af59df2827599cecbaa4f054e1ccf1
-size 1524716

audio/21.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ff2b40d06add3f07ca26e609ca0fef0270b9f4e72bbfe33a31bf193bcee7e96b
-size 4384556

audio/3.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2ea80e81bd6d3a5fd0addc1a52eca7496af6a382ff83a711675817d274b92300
-size 3605804
+oid sha256:a851078868d37b85ca594db6811fa7a9410764bf5e55f8de9ff57d0724843d98
+size 5389962

audio/4.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a851078868d37b85ca594db6811fa7a9410764bf5e55f8de9ff57d0724843d98
-size 5389962
+oid sha256:1c914d6e441095bd6a40aa5a558418ae5a321ccf83238b073b8f4f16c6154f39
+size 1672842

audio/5.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1c914d6e441095bd6a40aa5a558418ae5a321ccf83238b073b8f4f16c6154f39
-size 1672842
+oid sha256:736e83bcc12261e6d83c46b915a26d9c4fc4fbffdf441a4b6a1bc896300acf83
+size 649051

audio/6.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:736e83bcc12261e6d83c46b915a26d9c4fc4fbffdf441a4b6a1bc896300acf83
-size 649051
+oid sha256:5df94fb343c121e973b335952996dfee728aa804f5adfae688651654e30a1c1b
+size 2566124

audio/7.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5df94fb343c121e973b335952996dfee728aa804f5adfae688651654e30a1c1b
-size 2566124
+oid sha256:cc2e20543f4f7eada7275e0c7b9fc256023320eb7f067edc41e84ff81c5f633c
+size 3235436

audio/8.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4249ca4f032cf3a438c3f004a48ed2da00c563e603ef284c892302689999bb96
-size 2980844
+oid sha256:1a692a5efca0e78afd528ef4edcff7d69e64cbb5989ae20f31ad01cf2faeb271
+size 2577644

audio/9.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cc2e20543f4f7eada7275e0c7b9fc256023320eb7f067edc41e84ff81c5f633c
-size 3235436
+oid sha256:55a4246d9bf9bfdfc1b28b4add8b4a8c746b7473f10e28ff3f909709017b04eb
+size 2070764

transcripts/uncorrected/1.txt

@@ -1 +1 @@
-Okay, I'd like to create an app which does the following. The purpose of the app is to visualize how different countries, ideologies, systems approach common policy challenges. An example of a policy challenge that I'm just providing for explaining how I could see this working is second-hand smoke control. Some countries have very strict regulations, some countries have very lax enforcement. And probably there is not really much distinction by system of government but the user prompts it called policy visualizer and the user enters a policy challenge. So another example might be minimum alcohol purchasing laws.<br><br>Once Gemini receives this prompt, its task will be to research how different countries in the first instance approach this topic. And from that analysis, it can identify commonalities or clusters. The research process happens in the back end. And the user is shown some kind of progress indicators like researching what it's doing basically. Not a huge amount of verbosity but just a few cues so the user knows that it's not stuck or it's actually doing something.<br><br>Once Gemini concludes its first pass it will have grouped not necessarily every country in the world but based on the clusters it identifies it found groups. Each group is given a label. The label might be laissez-faire, permissive. These may be either recognized labels or what Gemini feels it's best to describe them as. And the countries are displayed with their national flags in alphabetical order.<br><br>The next functionality is that the user can click on the cluster and Gemini will describe what it is about this law that it considered them to be a cluster. In other words, the way in which they approach the challenge. That's a modal. Then the user can click on any country and it can see how that country approaches it. So I might click on the flag of Germany and either an accordion or a modal it show how Germany approaches in this case gun control and its cluster.<br><br>Country level is always a tab and only if there's other taxonomies. By taxonomy I mean that we think there's a very, Gemini says there's a very big difference and how different right-wing versus left-wing approaches we're going to do. We're going to create one more tab with that. But that should be kind of only if there's very compelling reason to do so. Or if it has significant data to share. So if it feels like there's enough data about how US states approach an issue at the state level, it might create a tab called US States and then follow the same pattern in which it groups them into clusters.<br><br>The objective is to, rather than searching through Google to see how different countries do different things, to start with your question and then get this visualisation. And I think the icing on the cake would be an analysis. So this is a visual presentation and then there may be analysis showing significant differences, some similarities. So there's like a report, a textual report, but the main tab, because I think it's the most interesting one, is the visualization, the policy visualizer.
+So what I would like to do in this is create an app really for the purpose of demonstrating the capabilities of audio input as a modality because I think it's overlooked and it brings a lot of really interesting use cases.<br><br>What I'd like to do for this one is, as one facet of it, the user uploads a recording. It should be a recording of just one speaker. And upon receiving the recording, it'll be ingested to Gemini. and Gemini will analyse it for the following. It will try to categorise the speaker's accent. It will estimate the words per minute at which they speak. And then it will provide a phonetic analysis, basically a linguistic analysis of their speech, how they pronounce certain and many others.<br><br>A voice clip, Gemini processes it and then it produces a detailed analysis in a nicely displayed manner.

transcripts/uncorrected/10.txt

@@ -1 +1 @@
-I'd like to consider a wee factor and then just give me your thoughts about this so currently it's a file based backend what I was wondering is would it make more sense to have a lightweight database backend SQLite let's say and and the important part of the utility which is the Hugging Face dataset push is what I'm using for the classification model would actually be a job whereby locally it will create the dataset from the local backend.<br><br>In other words, rather than having this sit in place as files, it's going to be constructed periodically. Basically when I say okay I've uploaded another batch, let's push, would that be easier and more logical to integrate with the front end?
+Okay, I'd like to create a sustainability report parser which will operate as follows. The user will provide a link to a sustainability disclosure or better they will upload a PDF. That's the expectation.<br><br>Upon receiving the PDF from the user the app will load the PDF in a frame. Gemini will identify on which page sustainability, The disclosure data for Scope 321 emissions is reported. And the PDF will load up in the frame, the viewer, with that page skipped to that page, and the data highlighted with a yellow overlay, slight highlight.<br><br>And beneath it Gemini will output the table for the top level in other words the summary of the scope 321 emissions with a short text description of what they were in summary the units detected scope 321 itemize then a disclaimer under that that this detection is based on automated processing may be incorrect and so on.

transcripts/uncorrected/11.txt

@@ -1 +0,0 @@
-Okay what I'd like to do is create an application with Gemini. The user will upload their resume and upon receiving the resume the purpose of this application is to ideate and many more. So, I'm going to show you how to create jobs, positions that the user might be suitable for. It could be what they've done previously or an extension of that, but it would also try to suggest alternative directions, as in slide pivots or rigby pig pivots.<br><br>They'll frame its suggestions with job title as in if the user uploads their resume they'll say oh you could be an AI product manager, salary range for this position. The user might also maybe the user should provide where they based though that should be obvious from the CV. So try to contextualize that by their area demand who hires for it analysis why this could be a cool job for you. Knowledge gaps slash upskilling, how you might want to upskill to qualify yourself for this job. Keywords that this job might be that you might find opportunities using these keywords. A certification, certifications that I want to pursue.<br><br>Then a kind of a Tinder interface, and so on. So, it's a really nice, thumbs up, thumbs down, and those are recorded in memory so that the user can go back through the suggestions that it liked. So it's kind of a career ideation tool really, career pivot ideation tool for the user to explore alternative directions if they're feeling like they might not be thinking very sufficiently widely about what it is that they could be using their skills for.

transcripts/uncorrected/12.txt

@@ -1 +0,0 @@
-Here's an idea for a product I had. Tell me if you think it's ridiculous and if something like this has been attempted. So, speech-to-text transcription is amazing and I've become very dependent on it for voice typing. Unfortunately, on Linux and specifically, it's really tricky to find something that works at the operating system level. There are tools for Windows and Mac, and what I really need is something that will do it in any program. Not a browser extension, not an IDE extension, because then you're forever looking for does this tool have voice support. And you end up having, like what I have now, three or four Whisper subscriptions.<br><br>And many more. And you free yourself from the keyboard literally, you begin to want to use it at all your computers on my laptop. And some of them, my desktop can run a whisper, my laptop really can't. And you don't want to be spending a bunch of time provisioning separate environments.<br><br>So my idea is for a mini PC, think something like the Raspberry Pi or Orange Pi, but not presented as an enthusiast product so much as a little edge device and many more A box for all intents and purposes which runs on device a very efficient speech model like Whisper and it does on hardware local inference. Everything is optimized for this one workload. It has a USB out and the USB out it functions as a HID device and it sends the transcribed text and so on. Influence on the device and straight out USB.<br><br>What this means is you can plug your voice keyboard, which I think is obvious name, into anything. You can have it bound to your desktop for most of the time, you go away for traveling for a while, you pack your box. So it's really analogous to a keyboard.<br><br>Now what I was thinking to myself as a stupid idea is yes, you could do this stuff on device, you could use Claude, maybe it's too niche. But it could be quite creative for people who are really into voice typing and want a way to. And if it had Bluetooth support, your little box, your voice typing centerpiece could also work with your tablets, your phone and you could sort of extend around it.

transcripts/uncorrected/13.txt

@@ -1 +0,0 @@
-Another idea for Gemini app. Recipe modifier, you get a recipe. Gemini parses the recipe, structures the data. Then, using a nutritional database, attempts to calculate the total fat per serving and the fat per ingredient.<br><br>Then, this is an app for people like me who are trying to adhere to a low-fat diet. It remixes a recipe to either achieve a certain fat amount, as in under X grams of fat, or to just make a general reduction within reasonable bounds while still trying to keep the recipe the recipe.

transcripts/uncorrected/14.txt

@@ -1 +0,0 @@
-Google ID8 to Try would be one of the apps that connects with the Google Workspace services. Which I don't know, maybe they've circumvented their general cautiousness.<br><br>Like voice to email. You send an email, you record a voice memo, it transcribes it, it checks your contacts, it generates an email, it shows you a draft, is that okay, and then it sends.

transcripts/uncorrected/15.txt

@@ -1 +0,0 @@
-I'd like to create an app that does the following. The user will paste an image or multiple images into the image upload feature. It'll run it through Gemini and it will attempt to extract the following fields: Serial Number, Model Number, Manufacturer, in a text field it will OCR readable text, Country of Manufacture.<br><br>And then based upon the detected product, the manufacturer and the part number and the serial number, it will provide a one line description, it will provide a multi-line description, it will provide a spec sheet. It will provide a year of first released on the market, age in years based on first release minus the current time, correct to the nearest 8.1, one decimal place.<br><br>And deprecation level from almost deprecated, fully deprecated, RRP, still on market, the last of the checkbox. So it'll basically take an image and then extract all these fields based on the initial OCR and then based on the web search complementing that.

transcripts/uncorrected/16.txt

@@ -1 +0,0 @@
-I'd like to create an app that does the following. The user will paste a screenshot from their calendar or there's a text field for calendar entries for a certain time period. Below that there is a voice recorder. The voice recorder will let out the user to record a voice message, record, pause, stop, and or retake.<br><br>When the user is instructed to narrate their timesheet for the week, and the user can also select a date for week commencing, just to validate when the first date that they're referring to in this timesheet is. When those three fields are provided by the user they get sent to Gemini and Gemini will then generate a timesheet based upon the user description with activities per day.<br><br>The meeting information that was received will be added. So I might diarize specific meetings that were referenced. So combining the two sets of data. And finally based the user might if the user includes a time spent estimate how many hours were spent per day on a certain project or task it will then calculate the estimated total hours spent and then a summary section.<br><br>This will be provided as a document which is created in markdown with the user it's rendered in rich text on the screen and the user can click download and if they do that it'll download the timesheet as a markdown file with the title automatically file name timesheet for week commencing in machine readable case.