This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
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# Splinter

## Overview

The Splinter model was proposed in [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter
is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus
comprising Wikipedia and the Toronto Book Corpus.

The abstract from the paper is the following:

In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order
of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred
training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between
current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question
answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all
recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans
are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select
the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD
with only 128 training examples), while maintaining competitive performance in the high-resource setting.

Tips:

- Splinter was trained to predict answers spans conditioned on a special [QUESTION] token. These tokens contextualize
  to question representations which are used to predict the answers. This layer is called QASS, and is the default
  behaviour in the [`SplinterForQuestionAnswering`] class. Therefore:
- Use [`SplinterTokenizer`] (rather than [`BertTokenizer`]), as it already
  contains this special token. Also, its default behavior is to use this token when two sequences are given (for
  example, in the *run_qa.py* script).
- If you plan on using Splinter outside *run_qa.py*, please keep in mind the question token - it might be important for
  the success of your model, especially in a few-shot setting.
- Please note there are two different checkpoints for each size of Splinter. Both are basically the same, except that
  one also has the pretrained weights of the QASS layer (*tau/splinter-base-qass* and *tau/splinter-large-qass*) and one
  doesn't (*tau/splinter-base* and *tau/splinter-large*). This is done to support randomly initializing this layer at
  fine-tuning, as it is shown to yield better results for some cases in the paper.

This model was contributed by [yuvalkirstain](https://huggingface.co/yuvalkirstain) and [oriram](https://huggingface.co/oriram). The original code can be found [here](https://github.com/oriram/splinter).

## Documentation resources

- [Question answering task guide](../tasks/question-answering)

## SplinterConfig

[[autodoc]] SplinterConfig

## SplinterTokenizer

[[autodoc]] SplinterTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## SplinterTokenizerFast

[[autodoc]] SplinterTokenizerFast

## SplinterModel

[[autodoc]] SplinterModel
    - forward

## SplinterForQuestionAnswering

[[autodoc]] SplinterForQuestionAnswering
    - forward

## SplinterForPreTraining

[[autodoc]] SplinterForPreTraining
    - forward

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# Splinter

## Overview

The Splinter model was proposed in [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter
is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus
comprising Wikipedia and the Toronto Book Corpus.

The abstract from the paper is the following:

In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order
of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred
training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between
current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question
answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all
recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans
are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select
the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD
with only 128 training examples), while maintaining competitive performance in the high-resource setting.

Tips:

- Splinter was trained to predict answers spans conditioned on a special [QUESTION] token. These tokens contextualize
  to question representations which are used to predict the answers. This layer is called QASS, and is the default
  behaviour in the [`SplinterForQuestionAnswering`] class. Therefore:
- Use [`SplinterTokenizer`] (rather than [`BertTokenizer`]), as it already
  contains this special token. Also, its default behavior is to use this token when two sequences are given (for
  example, in the *run_qa.py* script).
- If you plan on using Splinter outside *run_qa.py*, please keep in mind the question token - it might be important for
  the success of your model, especially in a few-shot setting.
- Please note there are two different checkpoints for each size of Splinter. Both are basically the same, except that
  one also has the pretrained weights of the QASS layer (*tau/splinter-base-qass* and *tau/splinter-large-qass*) and one
  doesn't (*tau/splinter-base* and *tau/splinter-large*). This is done to support randomly initializing this layer at
  fine-tuning, as it is shown to yield better results for some cases in the paper.

This model was contributed by [yuvalkirstain](https://huggingface.co/yuvalkirstain) and [oriram](https://huggingface.co/oriram). The original code can be found [here](https://github.com/oriram/splinter).

## Documentation resources

- [Question answering task guide](../tasks/question-answering)

## SplinterConfig

[[autodoc]] SplinterConfig

## SplinterTokenizer

[[autodoc]] SplinterTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## SplinterTokenizerFast

[[autodoc]] SplinterTokenizerFast

## SplinterModel

[[autodoc]] SplinterModel
    - forward

## SplinterForQuestionAnswering

[[autodoc]] SplinterForQuestionAnswering
    - forward

## SplinterForPreTraining

[[autodoc]] SplinterForPreTraining
    - forward
