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# Wav2Vec2-Conformer

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## 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).

This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).

Note: Meta (FAIR) released a new version of [Wav2Vec2-BERT 2.0](https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert) - it's pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per [this guide](https://huggingface.co/blog/fine-tune-w2v2-bert).

## 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](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`.

## Resources

- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr)

## 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
