readme: ## 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.
  