transformers/docs/source/en/model_doc/wav2vec2-conformer.md

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.

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.

This model was contributed by patrickvonplaten. The original code can be found here.

Documentation 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