OSerror, when loading 'wav2vec2-large-xlsr-53' Model of Wav2vec2
## Environment info
- `transformers` version: 4.3.3
- Platform: Linux-4.15.0-29-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyTorch version (GPU?): 1.8.0 (False)
- Tensorflow version (GPU?): not installed (NA)


  @patrickvonplaten


## Information

Model I am using  Wav2vec2.0:

The problem arises when using:

Scipts:
import soundfile as sf
import torch
from transformers import AutoTokenizer, AutoModel,Wav2Vec2ForCTC, Wav2Vec2Tokenizer

tokenizer4 = AutoTokenizer.from_pretrained("facebook/wav2vec2-large-xlsr-53")
model4 = AutoModel.from_pretrained("facebook/wav2vec2-large-xlsr-53")

OSError: 
OSError: Can't load tokenizer for 'facebook/wav2vec2-large-xlsr-53'. Make sure that:

- 'facebook/wav2vec2-large-xlsr-53' is a correct model identifier listed on 'https://huggingface.co/models'

- or 'facebook/wav2vec2-large-xlsr-53' is the correct path to a directory containing relevant tokenizer files


The tasks I am working on is:
* an official wav2vec task: facebook/wav2vec2-large-xlsr-53

## To reproduce
Steps to reproduce the behavior:
Follow the instructions
https://huggingface.co/facebook/wav2vec2-large-xlsr-53


## Expected behavior

I try to use xlsr model as the pre-trained model to finetune my own ASR model, but the xlsr model, especially tokenizer, can't be loaded smoothly. Could you tell me how to modify it? Thank you very much! 

The model doesn't contains the tokenizer and preprocessing files.
Checkout this notebook:
https://huggingface.co/blog/fine-tune-xlsr-wav2vec2

To build your own vocab etc<|||||>@flozi00 Thank you! 

> The model doesn't contains the tokenizer and preprocessing files.
> Checkout this notebook:
> https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
> 
> To build your own vocab etc

<|||||>Forget about `transformers.AutoProcessor`. This class is used to load a general `processor` model. By call the constructor of this class you have to submit `feature_extractor` and `tokenizer`, however `Wav2Vec2` just extract the features from raw speech data. Then, there is no `tokenizer` has been defined for it. To load the `processor` you can use `transformers.Wav2Vec2FeatureExtractor` as follow:

```
from transformers import Wav2Vec2FeatureExtractor

processor = Wav2Vec2FeatureExtractor.from_pretrained('facebook/wav2vec2-large-xlsr-53')
```<|||||>> Forget about `transformers.AutoProcessor`. This class is used to load a general `processor` model. By call the constructor of this class you have to submit `feature_extractor` and `tokenizer`, however `Wav2Vec2` just extract the features from raw speech data. Then, there is no `tokenizer` has been defined for it. To load the `processor` you can use `transformers.Wav2Vec2FeatureExtractor` as follow:
> 
> ```
> from transformers import Wav2Vec2FeatureExtractor
> 
> processor = Wav2Vec2FeatureExtractor.from_pretrained('facebook/wav2vec2-large-xlsr-53')
> ```

Using this approach, I got segmentation fault on the same wav2vec2-large-xlsr-53 model.
Output:
```bash
Some weights of the model checkpoint at facebook/wav2vec2-large-xlsr-53 were not used when initializing Wav2Vec2Model: ['quantizer.weight_proj.bias', 'project_q.weight', 'project_q.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_hid.bias', 'project_hid.weight']
- This IS expected if you are initializing Wav2Vec2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing Wav2Vec2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Processing 24393.wav
Segmentation fault (core dumped)
```