Enabling live `automatic-speech-recognition` asr for Whisper.
Author: Narsil
Pull request: yes

# 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("")
```

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR 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:

- 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

 -->

Comments:
_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 ?

@Narsil 
I'm testing it on Mac M2 Pro and it's not working. How can I fix this?
```bash
Start talking...
2024-05-11 12:56:44.036 ffmpeg[5850:59141] WARNING: Add NSCameraUseContinuityCameraDeviceType to your Info.plist to use AVCaptureDeviceTypeContinuityCamera.
2024-05-11 12:56:44.236 ffmpeg[5850:59141] WARNING: AVCaptureDeviceTypeExternal is deprecated for Continuity Cameras. Please use AVCaptureDeviceTypeContinuityCamera and add NSCameraUseContinuityCameraDeviceType to your Info.plist.
```