Adding support for `fp16` for asr pipeline.
Author: Narsil
Pull request: yes

# What does this PR do?

Fixes #20862 

Many things were considered before settling for this design.

- `feature_extractor(return_tensors="pt¨, torch_dtype=torch_dtype)` . This would have the advantage of being consistent, but not all feature extractors to define this, so it would affect all of them. Then why would we use `torch_dtype` instead of the more common place `dtype` which could be applied to TF and flax as well. Also it feels a bit redundant to specify both` return_tensors` and `torch_dtype`, it would be a good candidate to fuse both parameters (but outisde the scope of this PR).
- `AutoFeatureExtractor.from_pretrained(..., torch_dtype=torch_dtype)`. This would have the advantage of being overall so users don't need to respecify on each call. However we can't specifiy `return_tensors="pt" ` in there either, so for consistency I didn't try to put it there.
- `ffmpeg_read(..., dtype=dtype)` This would be nice to load directly the waveform into fp16 and just let fp16 flow through the feature_extractor. However, whisper in particular uses mel_spectrogram, so using f16 sound might actually damage performance.

In the end, this solution is the simplement I could come up with. Let `torch_dtype` flow to the pipeline, use it as a regular parameter and convert the output of the feature_extractor after.

This does incur a potentially extra copy but there's no risk of damaging quality of the input.

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Fixes # (issue)


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