Update README.md

README.md

@@ -1,48 +1,48 @@
----
-language: "fr"
-thumbnail:
-tags:
-- wav2vec2
-license: "apache-2.0"
----
-
-# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
-
-  
-LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. 
-
-For more information about our gender study for SSL moddels, please refer to our paper at: [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems]()
-
-  
-## Model and data descriptions
-
-We release four gender-specific models trained on 1K hours of speech.
-
-- [wav2vec2-FR-1K-Male-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-large/)
-- [wav2vec2-FR-1k-Male-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-base/)
-- [wav2vec2-FR-1K-Female-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-large/)
-- [wav2vec2-FR-1K-Female-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-base/)
-
-## Intended uses & limitations
-
-Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced.
-
-## Referencing our gender-specific models
-```
-@article{boito2022study,
-  title={A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems},
-  author={Marcely Zanon Boito and Laurent Besacier and Natalia Tomashenko and Yannick Est{\`e}ve}, 
-  journal={arXiv preprint arXiv:2204.01397},
-  year={2022}
-}
-```
-## Referencing LeBenchmark
-
-```
-@inproceedings{evain2021task,
-  title={Task agnostic and task specific self-supervised learning from speech with \textit{LeBenchmark}},
-  author={Evain, Sol{\`e}ne and Nguyen, Ha and Le, Hang and Boito, Marcely Zanon and Mdhaffar, Salima and Alisamir, Sina and Tong, Ziyi and Tomashenko, Natalia and Dinarelli, Marco and Parcollet, Titouan and others},
-  booktitle={Thirty-fifth  Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
-  year={2021}
-}
+---
+language: "fr"
+thumbnail:
+tags:
+- wav2vec2
+license: "apache-2.0"
+---
+
+# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
+
+  
+LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. 
+
+For more information about our gender study for SSL moddels, please refer to our paper at: [A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems](https://arxiv.org/abs/2204.01397)
+
+  
+## Model and data descriptions
+
+We release four gender-specific models trained on 1K hours of speech.
+
+- [wav2vec2-FR-1K-Male-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-large/)
+- [wav2vec2-FR-1k-Male-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Male-base/)
+- [wav2vec2-FR-1K-Female-large](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-large/)
+- [wav2vec2-FR-1K-Female-base](https://huggingface.co/LeBenchmark/wav2vec-FR-1K-Female-base/)
+
+## Intended uses & limitations
+
+Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced.
+
+## Referencing our gender-specific models
+```
+@article{boito2022study,
+  title={A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems},
+  author={Marcely Zanon Boito and Laurent Besacier and Natalia Tomashenko and Yannick Est{\`e}ve}, 
+  journal={arXiv preprint arXiv:2204.01397},
+  year={2022}
+}
+```
+## Referencing LeBenchmark
+
+```
+@inproceedings{evain2021task,
+  title={Task agnostic and task specific self-supervised learning from speech with \textit{LeBenchmark}},
+  author={Evain, Sol{\`e}ne and Nguyen, Ha and Le, Hang and Boito, Marcely Zanon and Mdhaffar, Salima and Alisamir, Sina and Tong, Ziyi and Tomashenko, Natalia and Dinarelli, Marco and Parcollet, Titouan and others},
+  booktitle={Thirty-fifth  Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+  year={2021}
+}
 ```