## Model and data descriptions

This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in [Boito et al., 2022](https://arxiv.org/abs/2201.05051).
**This is not an ASR fine-tuned model. There is no vocabulary file.**

## Intended uses & limitations

Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. 

## Referencing our IWSLT models

```
@article{boito2022trac,
  title={ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks},
  author={Boito, Marcely Zanon and Ortega, John and Riguidel, Hugo and Laurent, Antoine and Barrault, Lo{\"\i}c and Bougares, Fethi and Chaabani, Firas and Nguyen, Ha and Barbier, Florentin and Gahbiche, Souhir and others},
  journal={IWSLT},
  year={2022}
}
```
{}
transformers pytorch wav2vec2 pretraining arxiv:2201.05051 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us 

## Model and data descriptions

This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.
This is not an ASR fine-tuned model. There is no vocabulary file.

## Intended uses & limitations

Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. 

## Referencing our IWSLT models


## Model and data descriptions

This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.
This is not an ASR fine-tuned model. There is no vocabulary file. ## Intended uses & limitations

Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. ## Referencing our IWSLT models
TAGS
#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us 
 ## Model and data descriptions

This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.
This is not an ASR fine-tuned model. There is no vocabulary file. ## Intended uses & limitations

Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. ## Referencing our IWSLT models
passage: TAGS
#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us 
## Model and data descriptions

This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.
This is not an ASR fine-tuned model. There is no vocabulary file.## Intended uses & limitations

Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations.## Referencing our IWSLT models