Update README.md

README.md

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 # Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings
 
-[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
+Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
 
-Wav2Vec2 Conformer with rotary position embeddings, pretrained and fine-tuned on 960h hours of Librispeech and fine-tuned on 100 hours of Librispeechh on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
+**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
 
-[Paper (TODO)](https://arxiv.org/abs/2006.11477)
+**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
 
-Authors: ...
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-**Abstract**
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-...
+The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).
 
 The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
 
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 # Usage
 
 To transcribe audio files the model can be used as a standalone acoustic model as follows: