[Wav2Vec2Conformer] Official release (#17709)

* [Wav2Vec2Conformer] Official release

* remove from not-in-readme

docs/source/en/model_doc/wav2vec2-conformer.mdx

@@ -14,12 +14,18 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
+The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
+
+The official results of the model can be found in Table 3 and Table 4 of the paper.
+
 The Wav2Vec2-Conformer weights were released by the Meta AI team within the [Fairseq library](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models).
 
 Tips:
 
 - Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block
   as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100).
+- For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields 
+an improved word error rate.
 - Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2.
 - Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or
   rotary position embeddings by setting the correct `config.position_embeddings_type`.