India's 2024 AI landscape saw six breakthrough models: BharatGen's e-vikrAI for e-commerce, Sarvam-1 supporting 10 Indian languages, NVIDIA's Nemotron-4-Mini-Hindi-4B, AI4Bharat's Chitralekha for video transcreation, Everest 1.0 covering 35 languages, and Surya OCR for document processing. These models integrate local languages and cultural context, setting global AI benchmarks.

Researchers at the University of Hawaiʻi have developed deep learning models to analyze data from the world's most powerful solar telescope, the NSF Inouye Solar Telescope. Part of the SPIn4D project, the AI models can map the sun's 3D atmosphere in near real-time, processing tens of terabytes of daily data. Trained on 120TB of simulated data, the models aim to improve solar storm prediction and space weather monitoring.

India's AI startup ecosystem is rapidly growing, ranking 3rd globally. With 77% of startups investing in AI, ML, IoT, and blockchain, and $560 million raised in 2024, India leads in AI confidence and spending. Tier II and III cities are emerging as innovation hubs, while government initiatives like Digital India further accelerate growth, positioning India to lead the next wave of global AI innovation.

Telugu LLM Labs, led by researchers from LlamaIndex, is advancing NLP for Telugu — a language with 100M+ speakers historically underrepresented in AI. The initiative creates open datasets in both native and Romanized Telugu scripts and fine-tunes LLMs like Llama 2, Mistral, and TinyLlama, setting a precedent for other regional Indian languages in AI development.

asasdasdasdasdasdasdbout Model This repository contains a Fastspeech2 Model for 16 Indian languages (male and female both) implemented using the Hybrid Segmentation (HS) for speech synthesis. The model is capable of generating mel-spectrograms from text inputs and can be used to synthesize speech. Fs2 is composed of 6 feed-forward Transformer blocks with multi-head self-attention and 1D convolution on both phoneme encoder and mel-spectrogram decoder. In each feed-forward Transformer, the hidden size of multi-head attention is set to 256 and the number of head is set to 2. The kernel size of 1D convolution in the two-layer convolution network is set to 9 and 1, and the input/output size of the number of channels in the first and the second layer is 256/1024 and 1024/256. The duration predictor and variance adaptor, which are composed of stacks of several convolution networks and the final linear projection layer. The convolution layers of the duration predictor and variance adaptor are set to 2 and 5, the kernel size is set to 3, the input/output size of all layers is 256/256, and the dropout rate is set to 0.5.