Wav2vec2Processor normalization issues on transformers 4.10.0
When fine-tuning `facebook/wav2vec2-large-robust-ft-swbd-300h` I noticed I couldn't reproduce past training results from transformers version 4.9.2 now on 4.10. I noticed that inputs are not being correctly normalized with zero mean and unit variance in this new version. This seems to happen when `return_attention_mask=True`, audios in a batch input have different lengths and no padding is done.

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.10.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.6.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@patrickvonplaten 
@sgugger
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 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:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @Rocketknight1

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten 
- tokenizers: @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

Model hub:

- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->

## Information

Model I am using (Bert, XLNet ...): Wav2Vec 2.0

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Load Wav2Vec2Processor from `facebook/wav2vec2-large-robust-ft-swbd-300h`
2. Call processor with batched inputs of individual different lengths

Sample code to replicate the error:
```
import numpy as np
from transformers import Wav2Vec2Processor

processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h")

sample_rate = 16000
length_1 = 10
length_2 = 20

# Generate dummy input audios of same sample rate but different lengths
input_1 = np.random.rand((sample_rate * length_1))
input_2 = np.random.rand((sample_rate * length_1))
input_3 = np.random.rand((sample_rate * length_2))
 
same_length_result = processor([input_1, input_2], sampling_rate=sample_rate)
different_length_result = processor([input_1, input_3], sampling_rate=sample_rate)

# Show normalized batched audios when using same length
print(same_length_result)
# Show normalized batched audios when using different length
print(different_length_result)

# Check same audio suffers different transformations according to length of audios in batch
np.testing.assert_array_equal(same_length_result["input_values"][0], different_length_result["input_values"][0])
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

## Expected behavior
A successful assert. Both processed inputs should be equal, with a mean close to 0 and a standard deviation close to 1.
<!-- A clear and concise description of what you would expect to happen. -->

Hey @dmurillo976s,

Thanks a lot for the very well explained issue! I can reproduce the problem. I'll open a PR to fix it today<|||||>@dmurillo976s  - this PR: https://github.com/huggingface/transformers/pull/13512 should fix the problem. Could you give it a try? :-)<|||||>Hi @patrickvonplaten,
Thank you so very much! Sorry for not responding earlier. I've tried the latest patch release version and everything works as it should!