## 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.

{}
transformers pytorch wav2vec2 feature-extraction arxiv:2107.07402 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us 

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


Original Repo contains models in fairseq format.


Languages in the pretraining dataset
------------------------------------



Repo for training:
------------------


Experimentation platform built on top of fairseq.



TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #arxiv-2107.07402 #endpoints_compatible #region-us 
