add VITS model
# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [x] 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.

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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/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

 -->

Notes about the tokenizer:

1. This is not the VITS tokenizer but the one for MMS-TTS.
2. The vocab doesn't have padding (or unknown) tokens in it, but uses token_id 0 for this. That breaks on the HF tokenizers because it will split the input text on the padding token, so if I set `pad_token_id = 0` then the letters that token_id 0 corresponds to will disappear from the text.
3. To fix this issue, I'm adding `<pad>` and `<unk>` to the vocab, but then in the model we set such token_ids to 0 before feeding the input into the first layer. It's a bit hacky. Ideas for a nicer solution are appreciated.
4. The tokenizer also inserts an additional token_id 0 in between every token. No idea why but that's how it works.
<|||||>The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_24085). All of your documentation changes will be reflected on that endpoint.<|||||>This is ready for a first review yet.

Two checkpoints are currently available:

* https://huggingface.co/Matthijs/mms-tts-eng
* https://huggingface.co/Matthijs/mms-tts-nld

Small usage example:

```
from transformers import VitsMmsTokenizer, VitsModel
import torch

tokenizer = VitsMmsTokenizer.from_pretrained("Matthijs/mms-tts-eng")
model = VitsModel.from_pretrained("Matthijs/mms-tts-eng")
    
inputs = tokenizer(text="Hello, my dog is cute", return_tensors="pt")

outputs = model(inputs["input_ids"])
speech = outputs.audio
```

The current model is the MMS-TTS version, not the original VITS version. The conversion scripts can handle both, but for original VITS support the tokenizer is still missing.

Still needs to be done: 

* tests
* tokenizer for actual VITS 

@Vaibhavs10 For this review, in particular could you verify the names of the layers in the flow layers etc make sense? Thanks!
<|||||>Some of the MMS-TTS checkpoints require the use of the tool `uromanize` from https://github.com/isi-nlp/uroman to convert the input script into the Latin alphabet. Since this is a separate Perl script, it is not included in Transformers and the user will have to run `uromanize.pl` themselves before using the tokenizer.<|||||>> I'm not too sure why I'm asked for a review here as all comments from @sanchit-gandhi are being ignored. 

No they aren't?! I've integrated most of his suggestions and replied with counterarguments otherwise.
<|||||>Tokenizer can now handle both the original VITS models (which require phonemization) and the MMS-TTS models.<|||||>Hey @sgugger / @amyeroberts  - this one is ready for a review! We've got one open discussion around variable namings: https://github.com/huggingface/transformers/pull/24085#discussion_r1243884355

But otherwise the comments have been resolved and the code cleaned-up. Please address any comments / suggestions to myself, as I'll be taking over this PR for the rest of the integration<|||||>Would be really great to get your review here @amyeroberts! We're aiming to have this model feature as part of the next Unit of the audio transformers course 🤗 https://github.com/huggingface/audio-transformers-course/pull/61<|||||>This is ready for a second look @amyeroberts