Allow dict input for audio classification pipeline
# What does this PR do?

Allow dictionary inputs for the audio classification pipeline

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- 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
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- accelerate: [different repo](https://github.com/huggingface/accelerate)
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- 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._<|||||>Apologies @sgugger! To clarify, the changes in this PR are one-for-one copied from the input arguments in https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/automatic_speech_recognition.py

Essentially, the PR allows users to input a dictionary of inputs to the pipeline. This aligns the pipeline with `datasets`, where the `audio` column returns a dict with `array` (the 1-d audio array) and `sampling_rate` (the sampling rate of the audio):
```python
from datasets import load_dataset

librispeech = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
librispeech[0]["audio"]
```
**Output:**
```
{'path': '/Users/sanchitgandhi/.cache/huggingface/datasets/downloads/extracted/aad76e6f21870761d7a8b9b34436f6f8db846546c68cb2d9388598d7a164fa4b/dev_clean/1272/128104/1272-128104-0000.flac',
 'array': array([0.00238037, 0.0020752 , 0.00198364, ..., 0.00042725, 0.00057983,
        0.0010376 ]),
 'sampling_rate': 16000}
```
(the `path` column is deprecated an no longer required, but retained for backwards compatibility. This is what removing `path` refers to in the PR)

This PR enables the dict to be passed directly to the pipeline, in the same way that we do for the ASR pipeline and the `transformers` feature extractors:
```python
pred_labels = pipe(librispeech[0]["audio"])
```

If there are any API decisions you feel require changing, I'd be happy to update these in the original code before propagating to this file.<|||||>I think what you're trying to do is already supported, but the sampling rate needs to be in the same dict as the array (both are needed to represent a single audio).

That being said, the errors raised when misusing this feature could probably be largely improved (to guide users towards the correct form).