Create README.md

README.md

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+## Overview
+
+We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
+lingual speech representations from raw audio across **23 Indic languages**. It is built on top of wav2vec
+2.0 which is solved by training a contrastive task over masked latent speech representations and
+jointly learns the quantization of latents shared across all languages.
+
+[Arxiv Link](https://arxiv.org/pdf/2107.07402.pdf)
+
+[Original Repo](https://github.com/Open-Speech-EkStep/vakyansh-models) contains models in fairseq format.
+
+## Languages in the pretraining dataset
+
+| Language  | Data (In Hrs) |
+|-----------|---------------|
+|  Assamese | 254.9         |
+|  Bengali  | 331.3         |
+|   Bodo    | 26.9          |
+|   Dogri   | 17.1          |
+|  English  | 819.7         |
+|  Gujarati | 336.7         |
+|   Hindi   | 4563.7        |
+|  Kannada  | 451.8         |
+|  Kashmiri | 67.8          |
+|  Konkani  | 36.8          |
+|  Maithili | 113.8         |
+| Malayalam | 297.7         |
+|  Manipuri | 171.9         |
+|  Marathi  | 458.2         |
+|   Nepali  | 31.6          |
+|    Odia   | 131.4         |
+|  Punjabi  | 486.05        |
+|  Sanskrit | 58.8          |
+|  Santali  | 6.56          |
+|   Sindhi  | 16            |
+|   Tamil   | 542.6         |
+|   Telugu  | 302.8         |
+|    Urdu   | 259.68        |
+
+## Repo for training:
+
+[Experimentation](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation) platform built on top of fairseq.