Timestamps in AutomaticSpeechRecognitionPipeline not aligned in sample space
## Environment info
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- `transformers` version: 4.17.0.dev0
- Platform: Linux-5.10.98-1-MANJARO-x86_64-with-glibc2.33
- Python version: 3.9.8
- PyTorch version (GPU?): 1.10.2+cpu (False)
- Tensorflow version (GPU?): 2.8.0 (False)
- Flax version (CPU?/GPU?/TPU?): 0.4.0 (cpu)
- Jax version: 0.3.1
- JaxLib version: 0.3.0
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
@Narsil
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## Issue

Generating timestamps in the `AutomaticSpeechRecognitionPipeline` does not match the timestamps generated from `Wav2Vec2CTCTokenizer.decode()`. The timestamps from the pipeline are exceeding the duration of the audio signal because of the strides.

## Expected behavior

Generating timestamps using the pipeline gives the following prediction IDs and offsets:

```python
pred_ids=array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 12, 14, 14,  0,  0,
        0, 22, 11,  0,  7,  4, 15, 16,  0,  0,  4, 17,  5,  7,  7,  4,  4,
       14, 14, 18, 18, 15,  4,  4,  0,  7,  5,  0,  0, 13,  0,  9,  0,  0,
        0,  8,  0, 11, 11,  0,  0,  0, 27,  4,  4,  0, 23,  0, 16,  0,  5,
        7,  7,  0, 25, 25,  0,  0, 22,  7,  7, 11,  0,  0,  0, 10, 10,  0,
        0,  8,  0,  5,  5,  0,  0, 19, 19,  0,  4,  4,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  5,  0,  6,  4,  4,  0, 25,  0,
       14, 14, 16,  0,  0,  0,  0, 10,  9,  9,  0,  0,  0,  0, 25,  0,  0,
        0,  0,  0, 26, 26, 16, 12, 12,  0,  0,  0, 19,  0,  5,  0,  8,  8,
        4,  4, 27,  0, 16,  0,  0,  4,  4,  0,  3,  3,  0,  0,  0,  0,  0,
        0,  0,  0,  4,  4, 17, 11, 11, 13,  0, 13, 11, 14, 16, 16,  0,  6,
        5,  6,  6,  4,  0,  5,  5, 16, 16,  0,  0,  7, 14,  0,  0,  4,  4,
       12,  5,  0,  0,  0, 26,  0, 13, 14,  0,  0,  0,  0, 23,  0,  0, 21,
       11, 11, 11,  0,  5,  5,  5,  7,  7,  0,  8,  0,  4,  4,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0, 12, 21, 11, 11, 11,  0,  4,  4,  0,  0, 12, 11, 11,  0,  0,  0,
        0,  0,  7,  7,  5,  5,  0,  0,  0,  0, 23,  0,  0,  8,  0,  0,  4,
        4,  0,  0,  0,  0, 12,  5,  0,  0,  4,  4, 10,  0,  0, 24, 14, 14,
        0,  7,  7,  0,  8,  8, 10, 10,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0, 26,  0,  5,  0,  0,  0, 20,  0,  5,  0,
        0,  5,  5,  0,  0, 19, 16, 16,  0,  6,  6, 19,  0,  5,  6,  6,  4,
        4,  0, 14, 18, 18, 15,  4,  4,  0,  0, 25, 25,  0,  5,  4,  4, 19,
       16,  8,  8,  0,  8,  8,  4,  4,  0, 23, 23,  0, 14, 17, 17,  0,  0,
       17,  0,  5,  6,  6,  4,  4])
       
decoded['word_offsets']=[{'word': 'dofir', 'start_offset': 12, 'end_offset': 22}, {'word': 'hu', 'start_offset': 23, 'end_offset': 25}, {'word': 'mer', 'start_offset': 28, 'end_offset': 32
}, {'word': 'och', 'start_offset': 34, 'end_offset': 39}, {'word': 'relativ', 'start_offset': 42, 'end_offset': 60}, {'word': 'kuerzfristeg', 'start_offset': 63, 'end_offset': 94}, {'word'
: 'en', 'start_offset': 128, 'end_offset': 131}, {'word': 'zousazbudget', 'start_offset': 134, 'end_offset': 170}, {'word': 'vu', 'start_offset': 172, 'end_offset': 175}, {'word': '<unk>',
 'start_offset': 180, 'end_offset': 182}, {'word': 'milliounen', 'start_offset': 192, 'end_offset': 207}, {'word': 'euro', 'start_offset': 209, 'end_offset': 217}, {'word': 'deblokéiert', 
'start_offset': 221, 'end_offset': 249}, {'word': 'déi', 'start_offset': 273, 'end_offset': 278}, {'word': 'direkt', 'start_offset': 283, 'end_offset': 303}, {'word': 'de', 'start_offset': 311, 'end_offset': 313}, 
{'word': 'sportsbeweegungen', 'start_offset': 317, 'end_offset': 492}, {'word': 'och', 'start_offset': 495, 'end_offset': 499}, {'word': 'ze', 'start_offset': 503, 'end_offset': 507}, {'word': 'gutt', 'start_offset': 509, 'end_offset': 516}, 
{'word': 'kommen', 'start_offset': 519, 'end_offset': 532}]
```

However, the following is computed using `Wav2Vec2CTCTokenizer.decode()` and these offsets are expected:

```python
pred_ids=tensor([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 12, 14, 14,  0,  0,  0,
        22, 11,  0,  7,  4, 15, 16,  0,  0,  4, 17,  5,  7,  7,  4,  0, 14, 14,
        18, 18, 15,  4,  4,  0,  7,  5,  0,  0, 13,  0,  9,  0,  0,  0,  8,  0,
        11, 11,  0,  0,  0, 27,  4,  4,  0, 23,  0, 16,  0,  5,  7,  7,  0, 25,
        25,  0,  0, 22,  7,  7, 11,  0,  0,  0, 10, 10,  0,  0,  8,  0,  5,  5,
         0,  0, 19, 19,  0,  4,  4,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  5,  0,  6,  4,  4,  0, 25,  0, 14, 14, 16,  0,  0,  0,  0, 10,
         9,  9,  0,  0,  0,  0, 25,  0,  0,  0,  0,  0, 26, 26, 16, 12, 12,  0,
         0,  0, 19,  0,  5,  0,  8,  8,  4,  4, 27,  0, 16,  0,  0,  4,  4,  0,
         3,  3,  0,  0,  0,  0,  0,  0,  0,  0,  4,  4, 17, 11, 11, 13,  0, 13,
        11, 14, 16, 16,  0,  6,  5,  6,  6,  4,  0,  5,  5, 16, 16,  0,  0,  7,
        14,  0,  0,  4,  4, 12,  5,  0,  0,  0, 26,  0, 13, 14,  0,  0,  0,  0,
        23,  0,  0, 21, 11, 11, 11,  0,  5,  5,  5,  7,  7,  0,  8,  0,  4,  4,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0, 12, 21, 11, 11, 11,  0,  4,  4,  0,  0, 12, 11, 11,  0,  0,
         0,  0,  0,  7,  7,  5,  5,  0,  0,  0,  0, 23,  0,  0,  8,  0,  0,  4,
         4,  0,  0,  0,  0, 12,  5,  0,  0,  4,  4, 10,  0,  0, 24, 14, 14,  0,
         7,  7,  0,  8,  8, 10, 10,  0,  0,  0, 26,  5,  5,  0,  0,  0, 20,  5,
         0,  0,  0,  5,  0,  0, 19,  0, 16, 16,  6,  6, 19, 19,  5,  5,  6,  6,
         4,  0, 14,  0, 18, 15, 15,  4,  4,  0,  0, 25,  0, 16,  0,  4,  4, 19,
        16,  8,  0,  0,  8,  0,  4,  0,  0, 23,  0, 14,  0, 17,  0,  0,  0, 17,
         5,  5,  6,  6,  4,  4])
         
word_offsets=[{'word': 'dofir', 'start_offset': 12, 'end_offset': 22}, {'word': 'hu', 'start_offset': 23, 'end_offset': 25}, {'word': 'mer', 'start_offset': 28, 'end_offset': 32},  {'word': 'och', 'start_offset': 34, 'end_offset': 39}, {'word': 'relativ', 'start_offset': 42, 'end_offset': 60}, {'word': 'kuerzfristeg', 'start_offset': 63, 'end_offset': 94},  {'word': 'en', 'start_offset': 128, 'end_offset': 131}, {'word': 'zousazbudget', 'start_offset': 134, 'end_offset': 170},  {'word': 'vu', 'start_offset': 172, 'end_offset': 175}, {'word': '<unk>', 'start_offset': 180, 'end_offset': 182}, {'word': 'milliounen', 'start_offset': 192, 'end_offset': 207},  {'word': 'euro', 'start_offset': 209, 'end_offset': 217}, {'word': 'deblokéiert', 'start_offset': 221, 'end_offset': 249},  {'word': 'déi', 'start_offset': 273, 'end_offset': 278}, {'word': 'direkt', 'start_offset': 283, 'end_offset': 303},  {'word': 'de', 'start_offset': 311, 'end_offset': 313}, {'word': 'sportsbeweegungen', 'start_offset': 317, 'end_offset': 360}, {'word': 'och', 'start_offset': 362, 'end_offset': 367},  {'word': 'zu', 'start_offset': 371, 'end_offset': 374}, {'word': 'gutt', 'start_offset': 377, 'end_offset': 383},  {'word': 'kommen', 'start_offset': 387, 'end_offset': 400}]
```

## Potential Fix

A fix could be removing the strides instead of filling them with `first_letter` and `last_letter` in `apply_stride()`:

```python
def apply_stride(tokens, stride):

    input_n, left, right = rescale_stride(tokens, stride)[0]
    left_token = left
    right_token = input_n - right

    return tokens[:, left_token:right_token]
```

<!-- A clear and concise description of what you would expect to happen. -->

Hi @lemswasabi ,

Thanks for the report. We unfortunately cannot drop the stride as when there's batching involved, the tensors cannot be of different shapes. We can however keep track of the stride and fix the timestamps.

I'll submit a patch tomorrow probably.<|||||>Hi @Narsil,

Thanks for having a look at it.<|||||>@lemswasabi , in the end I aligned most of the code closer to what `ctc_with_lm` did (dropping the stride in  the postprocess method as I said, we can't in _forward).

There could still be some slight issue with timings, so the PR is not necessarily finished<|||||>@Narsil, I understand that dropping the stride has to be done in the postprocess method. I tested your patch and it works as expected now.<|||||>Great ! Please let us know if there's any further issue, ctc_with_lm coming right up :D