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