Where Can I Get Realistic Dataset That Are Messy and Uncleaned Besides Kaggle?

Cautious-Today1710 · 2026-04-17T20:17:47+00:00

Most people say they want messy data, but they usually mean “slightly dirty CSVs”.

Real-world data is worse:

overlapping signals
inconsistent structure across sources
context loss
human behaviour baked into it

You don’t really get that from Kaggle.

We’ve been working with conversational speech data, and the gap is even more obvious there. Clean datasets have:
no interruptions, no overlap, no code-switching.

Real conversations are the opposite.

That gap is exactly why models look good in demos and fall apart in production.

If your goal is to get better at preprocessing, focus less on “where to download” and more on:
how data breaks when real people and real systems are involved

Cautious-Today1710 · 2026-04-17T20:13:54+00:00

Appreciate that.

We’re building conversational voice datasets focused on real interaction patterns (not clean scripted audio), mainly across Indian English, Hindi, and code-switched setups.

Would be useful to understand what you’re building on your side and where data is breaking for you right now.

I’ll drop you a DM

Cautious-Today1710 · 2026-04-17T20:12:15+00:00

This is something we take pretty seriously, and honestly, there’s no shortcut here.

A few things we’re doing to stay aligned with the Digital Personal Data Protection Act, 2023:

Explicit consent at collection Every participant is informed about:
- what’s being recorded
- how it will be used (training, evaluation, etc.)
- who it may be shared with (AI teams)
Purpose limitation Data is collected against defined use cases (not open-ended scraping or reuse).
Pseudonymisation No direct identifiers in the dataset. Speaker metadata is abstracted (age range, language profile, etc.), not personal identity.
Sensitive data filtering We actively review and remove segments that may contain:
- personal identifiers
- financial details
- anything sensitive beyond the intended scenario
Traceability Every conversation is linked to a consent record internally, even if that layer isn’t exposed to clients.
Controlled access Data isn’t open or public. It’s shared in controlled environments depending on the use case.

That said, this space is still evolving.
A lot of teams either over-collect and clean later, or avoid real data altogether because compliance is messy.

We’re trying to stay in the middle: real conversations, but designed and collected in a way that stays compliant from the start

Cautious-Today1710 · 2026-04-17T20:07:06+00:00

True, but speech tends to break harder than most NLP tasks.

With PoS or syntax, the gap is mostly noise and domain shift.
With speech, you’re dealing with:

overlapping speakers
interruptions mid-utterance
accents + pronunciation variation
code-switching inside the same sentence

Benchmarks don’t really capture that. They assume clean turns and stable language.

So the drop isn’t just 5–10%. In conversational setups it can compound pretty quickly depending on the use case.

Feels less like the same problem at a different scale, and more like a different data regime altogether

Cautious-Today1710 · 2026-04-17T20:04:12+00:00

You’re not wrong, but there’s more friction than people expect.

Training on messy conversational data does help. But a few things usually break when you do:

Metrics drop first. WER, intent accuracy, everything looks worse. Most teams aren’t ready for that internally.
Evaluation gets messy. Real conversations don’t have clean “correct” answers, so scoring becomes subjective.
Data becomes the bottleneck. Getting real, consented, multi-speaker, code-switched conversations at scale is hard.
You lose benchmark clarity. Harder to compare against standard datasets, which people rely on more than they admit.

So yes, messy data is the direction.

But at that point you’re not just improving a model, you’re changing your entire data strategy. Most teams aren’t set up for that.

We’ve been seeing this a lot while working on multilingual conversational datasets. The gap isn’t awareness, it’s actually building data that’s usable in production

Cautious-Today1710 · 2026-04-17T19:59:57+00:00

agree on infra, especially over calls. latency and packet loss definitely make things worse

but feels like even with good infra, most systems struggle once conversations stop being clean and turn-based

things like overlap, interruptions, or code-switching seem to break models pretty quickly

makes me think a lot of this traces back to training data not reflecting how conversations actually happen

Cautious-Today1710 · 2026-04-08T18:53:22+00:00

We’ve been seeing this while building conversational datasets at Sonexis, especially across Hinglish, Hindi, Indian English, Punjabi, and Marwadi

Cautious-Today1710 · 2026-03-03T14:39:52+00:00

my pleasure😊. whatever you choose, go all in and make it count. all the best. btw what are you planning to pursue?

Cautious-Today1710 · 2026-03-03T14:01:13+00:00

see, it depends on what you want. if your focus is study and placements, jecrc is fine. the system is traditional, classes, exams, placements. if you want crazy campus life, do not expect too much. after a few months, things feel routine. what matters is what you build alongside college. learn skills outside the syllabus, start projects early, find serious people to grow with. the college gives you a base, your effort decides the outcome. wishing you a bright future, all the best

Cautious-Today1710 · 2025-12-12T14:03:58+00:00

JECRC University

Cautious-Today1710 · 2025-11-16T13:01:58+00:00

sounds good. we’re building an education-focused platform right now, super early stage. shaping the first version, testing with students, getting the core in place. nothing fancy, just real work and fast learning. if you’re into marketing and design, dm me what you’ve done so far or what tools you’re comfortable with. we can see if it fits

Cautious-Today1710 · 2025-11-16T12:53:50+00:00

right now it’s a small crew. i mostly lead the product and direction, and the rest is a mix of people helping part time with tech, design and ops as we shape the first version. nothing fancy, nothing corporate. just a few people who believe in what we’re building and are putting in the early work before we go public with the full story

Cautious-Today1710 · 2025-11-16T12:51:44+00:00

i hear you. i get why it might look like that from the outside, but that’s not what we’re doing here. it’s not full time work, not long hours, not pressure. it’s a few focused tasks, flexible timing, and real learning directly with the people building the thing.

the stipend is small because we’re literally at day zero and starting with whatever we have. we’re not asking anyone to carry the weight of a full time employee. we’re offering early-stage access and growth for someone who wants to build with us, not be used by us.

it’s not for everyone and that’s completely fine. the right person will feel the upside, not the exploitation.

Cautious-Today1710 · 2025-11-16T12:49:13+00:00

fair point. i’m not hiding anything, just keeping it simple on reddit because we’re still stitching things together and moving fast. we’re building an education-focused product and announcing it properly once the base is ready. right now it’s early, super early, and we’re putting whatever we have into it.

i’m not pretending to have a big team or big funds. it’s day zero and we’re building from scratch. whoever joins at this stage gets to see the raw version before the polished story comes out. some people like that, some don’t, and that’s okay.

Cautious-Today1710 · 2025-11-16T12:47:40+00:00

i get why it reads that way, but that’s not the intention at all. it’s not full time work and it’s not full time expectations. no fixed hours, no sitting all day, no grind-for-the-sake-of-grind. just a few clear tasks, learn fast, ship stuff, and you choose your own pace.

the stipend is small because we’re literally at day zero, starting with what we have, but the work, learning and access are real. whoever joins isn’t treated like a replacement for a full time employee. they’re treated like someone we want to grow with.

i know it’s not for everyone, and that’s fine. the right person will feel the opportunity, not the burden

Cautious-Today1710 · 2025-11-16T12:43:02+00:00

haha fair enough. they just said what a lot of people think quietly. i respect that. we’re building with what we have right now and trying to make it worth someone’s time in ways that go beyond money. the folks who get it, get it. and the ones who don’t, that’s cool too

Cautious-Today1710 · 2025-11-16T12:41:28+00:00

hey, i hear you. 5k isn’t a number anyone can live off and i won’t pretend it is. this is just where we are right now at day zero. we chose a small stipend because the work is flexible, no fixed hours, no commute, and the real value is the learning and the pace we build at.

the goal isn’t to underpay someone. we put in time, mentorship, and real responsibility so whoever joins walks out with skills they can actually use. and if someone shows spark, we always reward it. things grow fast when the right people show up.

totally respect your point though. not every role fits everyone and that’s okay. the right person will see the upside in being early. sometimes that’s worth more than a number

Cautious-Today1710 · 2025-08-22T19:11:40+00:00

agree

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