Updated model card for wav2vec2-conformer
Author: AshAnand34
Pull request: yes

# What does this PR do?

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This pull request significantly enhances the documentation for the `Wav2Vec2-Conformer` model by improving its structure, adding usage examples, and providing detailed explanations of its architecture and features. It also introduces quantization support and updates the API reference for better clarity.

### Documentation Enhancements:

* Reorganized the `Wav2Vec2-Conformer` documentation to include a more structured overview, detailed usage examples, and tips for applying the model to different tasks. Examples include automatic speech recognition and audio classification using both the `Pipeline` and `AutoModel` classes.
* Added a section on quantization, demonstrating how to use `bitsandbytes` for 8-bit model quantization to reduce memory usage.

### Architectural and Feature Details:

* Expanded the explanation of the `Wav2Vec2-Conformer` architecture, highlighting its use of Conformer blocks for improved speech recognition performance and its compatibility with Wav2Vec2 workflows.
* Included notes on position embedding types, tokenizer compatibility, and performance improvements, along with links to official results and related resources.

### API Reference Updates:

* Updated the API reference with detailed sections for `Wav2Vec2ConformerConfig`, `Wav2Vec2ConformerModel`, and other related classes, ensuring a clear

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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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 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

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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