| ## 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. | |