add istft decoder for VITS model
Author: tuanh123789
Pull request: yes

# What does this PR do?

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Add the ISTFT, MS_ISTFT, MB_ISTFT decoder for VITS model.   New decoder synthesized speech as natural as that synthesized by VITS while achieving a real-time factor of 0.066 on an Intel Core i7 CPU, 4.1 times faster than original VITS. Suitable for real-time and edge device applications
paper: https://arxiv.org/pdf/2210.15975
checkpoint:
istft: https://huggingface.co/anhnct/ljspeech_vits_istft
ms_istft: https://huggingface.co/anhnct/ljspeech_vits_ms_istft
mb_istft: https://huggingface.co/anhnct/ljspeech_vits_mb_istft

Using:
```
from transformers import VitsModel, AutoTokenizer
import torch
import numpy as np

model = VitsModel.from_pretrained("anhnct/ljspeech_vits_istft")
tokenizer = AutoTokenizer.from_pretrained("anhnct/ljspeech_vits_istft")

text = "Hey, it's Hugging Face on the phone"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs).waveform

data_np = output.numpy()
data_np_squeezed = np.squeeze(data_np)

import scipy

scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=data_np_squeezed)
```
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Comments:
Hey @tuanh123789, thanks for opening this PR!

In fact, I didn't know about this paper. It's really interesting, thank you for bringing it to light. 

That said, I don't think we shoud proceed with the integration, mostly because the ratio impact to effort is too low: I'm under the impression the new mechanisms don't have that much traction. I've seen no use of these novel mechanisms, both in the released pre-trained checkpoints and in potential new checkpoints. This means that it will potentially be used little or not at all. Add to this the burden of integration and maintenance and you should get my point!

Of course, I'm open to discuss this!

(you can still use your proposed checkpoints with your custom branch if you still want to use them)