Adding support for `microphone` streaming within pipeline.
- Uses `ffmpeg` to get microphone data.
- Makes sure alignment is made to `size_of_sample`.
- Works by sending `{"raw": ..data.., "stride": (n, left, right),
"partial": bool, "sampling_rate": sampling_rate}`
directly to the pipeline enabling to stream partial results and still
get inference.
- Let's `partial` information flow through the pipeline to enable caller
  to get it back and choose to display text or not.

- ~~The striding reconstitution is bound to have errors since CTC does not
keep previous state. Currently most of the errors are we don't know if
there's a space or not between two chunks.
Since we have some left striding info, we could use that during decoding
to choose what to do with those spaces and even extra letters maybe (if
the stride is long enough, it's bound to cover at least a few symbols)~~ Fixed by using intelligent replacement on the dropped `tokens`.

```python
import datetime
import sys
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_microphone_live

pipe = pipeline("automatic-speech-recognition", device=0)
sampling_rate = pipe.feature_extractor.sampling_rate


start = datetime.datetime.now()

chunk_length_s = 5
stream_chunk_s = 0.1
mic = ffmpeg_microphone_live(
    sampling_rate=sampling_rate,
    chunk_length_s=chunk_length_s,
    stream_chunk_s=stream_chunk_s,
)
print("Start talking...")
for item in pipe(mic):
    sys.stdout.write("\033[K")
    print(item["text"], end="\r")
    if not item["partial"][0]:
        print("")
```

2nd Better IMO, but low-level demo (requires curses on UNIX like, does not work on windows variants):
```python
import sys
import numpy as np
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_microphone_live
from curses import wrapper
import curses


def main():
    pipe = pipeline("automatic-speech-recognition", device=0)
    sampling_rate = pipe.feature_extractor.sampling_rate

    chunk_length_s = 5
    stream_chunk_s = 0.1
    mic = ffmpeg_microphone_live(
        sampling_rate=sampling_rate,
        chunk_length_s=chunk_length_s,
        stream_chunk_s=stream_chunk_s,  # , stride_length_s=(1, 0.1)
    )
    print("Start talking...")
    stdscr = curses.initscr()
    curses.noecho()
    curses.cbreak()
    text = ""
    for item in pipe(mic):
        displayed = text + item["text"]
        if not item["partial"][0]:
            text += item["text"]
        
        stdscr.addstr(0, 0, displayed)
        stdscr.clrtoeol()
        stdscr.refresh()


if __name__ == "__main__":
    wrapper(main())
```


# What does this PR do?

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Hey @Narsil,

I think the PR broke some slow tests:

```
FAILED tests/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_speech_to_text_leveraged
FAILED tests/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_torch_speech_encoder_decoder
FAILED tests/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_xls_r_from_en
FAILED tests/test_pipelines_automatic_speech_recognition.py::AutomaticSpeechRecognitionPipelineTests::test_xls_r_to_en
```

Could you take a look maybe? :-)<|||||>I can't reproduce. 

Is did have an issue with old `1.18.0` version, gone in `1.18.3`, was that it ?<|||||>Fixed it :-)