Fix docs warnings for conformer w2v2 (#2900)

Summary:
cc mthrok

Pull Request resolved: https://github.com/pytorch/audio/pull/2900

Reviewed By: mthrok

Differential Revision: D41839924

Pulled By: carolineechen

fbshipit-source-id: ba3ada7d04a86d99e08c9044de05a1c48b05d036

torchaudio/prototype/models/_conformer_wav2vec2.py

@@ -677,14 +677,14 @@ def conformer_wav2vec2_pretrain_base(
     cross_sample_negatives: int = 0,
 ) -> ConformerWav2Vec2PretrainModel:
     """Build Conformer Wav2Vec2 Model for pre-training with "small" architecture from
-    *Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks* :cite:`conformerssl`
+    *Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks* :cite:`9746490`
 
     Args:
         extractor_input_dim (int, optional): Input dimension of the features. (Default: 64)
         extractor_output_dim (int, optional): Output dimension after feature extraction. (Default: 256)
         encoder_projection_dropout (float, optional):
             The dropout probability applied after the input feature is projected to
-            ``embed_dim`. (Default: 0.0)
+            ``embed_dim``. (Default: 0.0)
         mask_prob (float, optional):
             Probability for each token to be chosen as start of the span to be masked. (Default: 0.3)
         mask_length (int, optional):
@@ -738,14 +738,14 @@ def conformer_wav2vec2_pretrain_large(
     cross_sample_negatives: int = 0,
 ) -> ConformerWav2Vec2PretrainModel:
     """Build Conformer Wav2Vec2 Model for pre-training with "large" architecture from
-    *Conformer-Based Slef-Supervised Learning for Non-Speech Audio Tasks* :cite:`conformerssl`
+    *Conformer-Based Slef-Supervised Learning for Non-Speech Audio Tasks* :cite:`9746490`
 
     Args:
         extractor_input_dim (int, optional): Input dimension of the features. (Default: 64)
         extractor_output_dim (int, optional): Output dimension after feature extraction. (Default: 256)
         encoder_projection_dropout (float, optional):
             The dropout probability applied after the input feature is projected to
-            ``embed_dim`. (Default: 0.0)
+            ``embed_dim``. (Default: 0.0)
         mask_prob (float, optional):
             Probability for each token to be chosen as start of the span to be masked. (Default: 0.3)
         mask_length (int, optional):