Wav2Vec2-Conformer
Overview
The Wav2Vec2-Conformer was added to an updated version of fairseq S2T: Fast Speech-to-Text Modeling with fairseq 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.
This model was contributed by patrickvonplaten.
The original code can be found here.
Usage 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.
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.

Resources

Audio classification task guide
Automatic speech recognition task guide

Wav2Vec2ConformerConfig
[[autodoc]] Wav2Vec2ConformerConfig
Wav2Vec2Conformer specific outputs
[[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
Wav2Vec2ConformerModel
[[autodoc]] Wav2Vec2ConformerModel
    - forward
Wav2Vec2ConformerForCTC
[[autodoc]] Wav2Vec2ConformerForCTC
    - forward
Wav2Vec2ConformerForSequenceClassification
[[autodoc]] Wav2Vec2ConformerForSequenceClassification
    - forward
Wav2Vec2ConformerForAudioFrameClassification
[[autodoc]] Wav2Vec2ConformerForAudioFrameClassification
    - forward
Wav2Vec2ConformerForXVector
[[autodoc]] Wav2Vec2ConformerForXVector
    - forward
Wav2Vec2ConformerForPreTraining
[[autodoc]] Wav2Vec2ConformerForPreTraining
    - forward