AutoProcessor.from_pretrained doesn't support MCTCT Models
### System Info

Not actually relevant, but included for completeness:

- `transformers` version: 4.29.1
- Platform: Linux-4.18.0-348.23.1.el8_5.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- Safetensors version: not installed
- PyTorch version (GPU?): 2.0.1 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): 0.6.1 (cpu)
- Jax version: 0.4.9
- JaxLib version: 0.4.9
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@sanchit-gandhi 

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```python
from transformers import AutoProcessor, MCTCTProcessor
mctc_proc1 = AutoProcessor.from_pretrained("speechbrain/m-ctc-t-large")
mctc_proc2 = MCTCTProcessor.from_pretrained("speechbrain/m-ctc-t-large")
print(f"AutoProcessor: {mctc_proc1}")
print(f"MCTCTProcessor: {mctc_proc2}")
```

The first line prints a `MCTCTProcessor` instance, containing a`MCTCTFeatureExtractor` feature extractor and `Wav2Vec2CTCTokenizer` tokenizer)  while the second prints just an `Wav2Vec2CTCTokenizer` instance.

### Expected behavior

`AutoProcessor.from_pretrained` should return an `MCTCTProcessor` instance when the provided model is an MCTCT model.

The reason it does not right now is because [the code for `AutoProcessor`](https://github.com/huggingface/transformers/blob/17a55534f5e5df10ac4804d4270bf6b8cc24998d/src/transformers/models/auto/processing_auto.py#LL42C1-L82C1) does not include a mapping entry for MCTCT.

```python
PROCESSOR_MAPPING_NAMES = OrderedDict(
    [
        ("align", "AlignProcessor"),
        ("altclip", "AltCLIPProcessor"),
        ("blip", "BlipProcessor"),
        ("blip-2", "Blip2Processor"),
        ("bridgetower", "BridgeTowerProcessor"),
        ("chinese_clip", "ChineseCLIPProcessor"),
        ("clap", "ClapProcessor"),
        ("clip", "CLIPProcessor"),
        ("clipseg", "CLIPSegProcessor"),
        ("flava", "FlavaProcessor"),
        ("git", "GitProcessor"),
        ("groupvit", "CLIPProcessor"),
        ("hubert", "Wav2Vec2Processor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mgp-str", "MgpstrProcessor"),
        ("oneformer", "OneFormerProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("pix2struct", "Pix2StructProcessor"),
        ("sam", "SamProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("speecht5", "SpeechT5Processor"),
        ("trocr", "TrOCRProcessor"),
        ("tvlt", "TvltProcessor"),
        ("unispeech", "Wav2Vec2Processor"),
        ("unispeech-sat", "Wav2Vec2Processor"),
        ("vilt", "ViltProcessor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("wav2vec2-conformer", "Wav2Vec2Processor"),
        ("wavlm", "Wav2Vec2Processor"),
        ("whisper", "WhisperProcessor"),
        ("xclip", "XCLIPProcessor"),
    ]
)
``` 

An [MCTCTProcessor class](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mctct/processing_mctct.py) exists whose `from_pretrained` function behaves appropriately. `AutoProcessor` should behave the same way, rather than falling back to a tokenizer.

The fix seems simple enough, by adding the entry below to `PROCESSOR_MAPPING_NAMES` (but I am far from an expert):

```python
("mctct", "MCTCTProcessor"),
```

For comparison, the `AutoModel.from_pretrained` method does support MCTCT and thus behaves appropriately because [its mapping contains a line for MCTCT](https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L125).
cc @sanchit-gandhi 