Enabling live `automatic-speech-recognition` asr for Whisper.
# What does this PR do?

Enables live ASR for Whisper.
Inference is slower for ASR than CTC models though, so these demos come with 
come caveat:

**IT will not be live on too small hardware**. Just because it will fall behind
in inference times.

Simplest fix would be to increase the `stream_chunk_s` parameter. That will reduce
the "liveliness" of the inference but would put less strain on the hardware.

Another more complex fix (outside of these short scripts) would be to keep track
of real time, and **skip** some inferences in the pipelines when we are too late.

Live script:

```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", model="openai/whisper-base", 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())

```


Simpler script:

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

pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", 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("")
```

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Fixes # (issue)


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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts and @NielsRogge
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

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

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

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_The documentation is not available anymore as the PR was closed or merged._<|||||>> LGTM but we could add your script somewhere no? Seems like it was asked

Where ? 
The `examples` is more focused on fine-tuning/training. It could be in the docstring of ASR pipeline ( which already contain tons of information).

Ultimately both examples are nice-to-have things, but definitely not something we want to support as the rest of core transformers iirc the discussions when this was added. Too many specific things, like capturing the correct mic is a hard job, then the amount of features that might be wanted could be very intense, and it's not really the goal of `tranformers` to maintain this. This is more a showcase and because using ffmpeg enables a relatively short code base for support.
Maybe something that could be more prominently be features in `speechbox`. @patrickvonplaten  for an opinion on this ?