Repository: huggingface/transformers
Pull request: [MINOR:TYPO] Update hubert.md

- typo fix (wave2vec instead of hubert)
- make code snippet copiable and runnable

# What does this PR do?

- Fix typo (wave2vec instead of hubert)
- Make code snippet copiable and runnable (torch wasn't imported and `device` wasn't set)

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@stevhliu @Vaibhavs10 

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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
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Library:

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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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- trainer: @muellerzr and @SunMarc
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- deepspeed: HF Trainer/Accelerate: @muellerzr
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Changed files:
filename: docs/source/en/model_doc/hubert.md
patch: @@ -71,9 +71,10 @@ pip install -U flash-attn --no-build-isolation
 Below is an expected speedup diagram comparing the pure inference time between the native implementation in transformers of `facebook/hubert-large-ls960-ft`, the flash-attention-2 and the sdpa (scale-dot-product-attention) version. We show the average speedup obtained on the `librispeech_asr` `clean` validation split: 
 
 ```python
->>> from transformers import Wav2Vec2Model
+>>> from transformers import HubertModel
+>>> import torch
 
-model = Wav2Vec2Model.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to(device)
+>>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to("cuda")
 ...
 ```