![[D] Offering licensed Indian language speech datasets (with explicit contributor consent)](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-neural-network-P6fqXULWLNUwjuxqUZnB3T.webp)
[D] Offering licensed Indian language speech datasets (with explicit contributor consent)
Hi everyone, I run a small data initiative where we collect speech datasets in multiple Indian languages directly from contributors who provide explicit consent for their recordings to be used and licensed. We can provide datasets with either exclusive or non-exclusive rights depending on the use case. The goal is to make ethically sourced speech data available for teams working on ASR, TTS, voice AI, or related research. If anyone here is working on speech models and might be looking for Indian language audio data, feel free to reach out. Happy to share more details about the datasets and collection process. — Divyam Founder, DataCatalyst datacatalyst.in submitted by /u/Trick-Praline6688 [link] [comments]
Could not retrieve the full article text.
Read on Reddit r/MachineLearning →Reddit r/MachineLearning
https://www.reddit.com/r/MachineLearning/comments/1sctehe/d_offering_licensed_indian_language_speech/Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelavailablerights
How to Start Linux Career After 12th – Complete Guide
If you're exploring How to Start Linux Career After 12th – Complete Guide, you're already choosing a smart and future-ready path. Linux is widely used in servers, cloud computing, and cyber security, which makes it one of the most in-demand skills in the IT industry. The best part is that you don’t need a technical degree to begin. With basic computer knowledge and consistent practice, you can start your journey right after completing your 12th. Why Choose Linux as a Career Linux is highly popular because companies use it to run secure and stable systems. It is free, powerful, and flexible, which makes it ideal for businesses and developers. Linux is used in web servers, mobile devices, and cloud platforms. Learning Linux also opens doors to high-paying career fields like DevOps and cyber
I built an AI fridge app that suggests Indian recipes before your food expires
The Problem I kept throwing away food because I forgot what was in my fridge. Sound familiar? What I Built FridgeSmart AI is a web app that: Tracks everything in your fridge and pantry Suggests Indian recipes based on what you already have Prioritizes ingredients that are about to expire Helps reduce food waste Tech Stack Frontend: React + Vite + TypeScript Backend: Node.js API Database: PostgreSQL (Neon) AI: Groq (Llama 3.3) Hosting: Render (free tier) Try It fridgesmart-ai-1.onrender.com Free to use — 3 recipe suggestions per day on the free plan. Would love your feedback!
Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability
Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability You validate your LLM outputs with Pydantic. The JSON is well-formed. The fields are correct. Life is good. Then your model returns a "polite decline" that says "I'd rather gouge my eyes out." It passes your type checks. It fails the vibe check. This is the Semantic Gap — the space between structural correctness and actual meaning . Every team shipping LLM-powered features hits it eventually. I got tired of hitting it, so I built Semantix . The Semantic Gap: Shape vs. Meaning Here's what most validation looks like today: class Response ( BaseModel ): message : str tone : Literal [ " polite " , " neutral " , " firm " ] This tells you the shape is right. It tells you nothing about whether the meaning is right.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
I built an AI fridge app that suggests Indian recipes before your food expires
The Problem I kept throwing away food because I forgot what was in my fridge. Sound familiar? What I Built FridgeSmart AI is a web app that: Tracks everything in your fridge and pantry Suggests Indian recipes based on what you already have Prioritizes ingredients that are about to expire Helps reduce food waste Tech Stack Frontend: React + Vite + TypeScript Backend: Node.js API Database: PostgreSQL (Neon) AI: Groq (Llama 3.3) Hosting: Render (free tier) Try It fridgesmart-ai-1.onrender.com Free to use — 3 recipe suggestions per day on the free plan. Would love your feedback!
Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability
Your LLM Passes Type Checks but Fails the "Vibe Check": How I Fixed AI Reliability You validate your LLM outputs with Pydantic. The JSON is well-formed. The fields are correct. Life is good. Then your model returns a "polite decline" that says "I'd rather gouge my eyes out." It passes your type checks. It fails the vibe check. This is the Semantic Gap — the space between structural correctness and actual meaning . Every team shipping LLM-powered features hits it eventually. I got tired of hitting it, so I built Semantix . The Semantic Gap: Shape vs. Meaning Here's what most validation looks like today: class Response ( BaseModel ): message : str tone : Literal [ " polite " , " neutral " , " firm " ] This tells you the shape is right. It tells you nothing about whether the meaning is right.
Building a Claude Agent with Persistent Memory in 30 Minutes
Every time you start a new Claude session, you’re paying an invisible tax. Re-explaining your project structure. Re-establishing your preferences. Re-seeding context that should have been remembered automatically. For a developer working on a long-running project, this amounts to hours of lost time per week — and a model that’s permanently operating below its potential because it’s always working from incomplete information. The Letta/MemGPT research (arXiv:2601.02163) first articulated this as the “LLM as OS” paradigm — the idea that a language model needs persistent, structured memory to operate as a genuine cognitive assistant rather than a stateless query engine. VEKTOR’s MCP server brings this paradigm to your local desktop in under 30 minutes. The MemGPT paper demonstrated that agent
AI Citation Registries and Provenance Absence Failure Modes
Why AI Produces Answers That Sound Right but Are Wrong How missing origin signals lead AI systems to assign authority incorrectly—and why explicit provenance encoding changes the outcome “Why does AI say the city issued a boil water notice when it actually came from the county?” The answer appears confidently structured, citing what looks like an official statement, but the attribution is wrong. The wording is accurate, the recommendation is correct, yet the authority has been reassigned. A city is presented as the issuer of a directive it never released. In a public safety context, this is not a minor formatting issue. It is a failure of origin, where the meaning of the information changes because the source has shifted. How AI Systems Separate Content from Source Artificial intelligence
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!