Update wav2vec2-bert model card
Author: AshAnand34
Pull request: yes

# What does this PR do?

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This pull request enhances the documentation for the Wav2Vec2-BERT model by improving its structure, adding usage examples, and providing detailed explanations of its features and configuration options. The changes aim to make the documentation more user-friendly and comprehensive for developers working with this model.

### Documentation Enhancements:

* **Introduction and Overview**:
  - Added a detailed introduction to Wav2Vec2-BERT, highlighting its multilingual capabilities, pre-training dataset, and downstream use cases like ASR and audio classification.

* **Usage Examples**:
  - Included Python code snippets demonstrating how to use the model with the `Pipeline` and `AutoModel` classes for tasks like speech recognition and audio classification.
  - Added an example of using 8-bit quantization to reduce memory usage with the `bitsandbytes` library.

* **Model Architecture and Features**:
  - Documented key architectural details, including the use of causal depthwise convolutional layers, mel-spectrogram inputs, and Conformer-based adapter networks.
  - Explained supported position embedding types and their configuration options.

* **API Reference Updates**:
  - Reorganized the API reference section for better readability, converting headings to a consistent format (e.g., `### Wav2Vec2BertConfig`) and maintaining a logical structure for model components. [[1]](diffhunk://#diff-fdae81694197d9e85451917d1e1eb8d1365b005b94b5e93bc4256b358f8732f0R144-R152) [[2]](diffhunk://#diff-fdae81694197d9e85451917d1e1eb8d1365b005b94b5e93bc4256b358f8732f0L71-R182)

* **Additional Notes**:
  - Added sections on training data, fine-tuning requirements, and links to official performance results for further exploration.

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#36979

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@stevhliu

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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface, @SunMarc and @qgallouedec
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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