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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# CTRL

<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>

## Overview

CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).

The abstract from the paper is the following:

*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.*

Tips:

- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
  or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
  more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
  observed in the *run_generation.py* example script.
- The PyTorch models can take the `past_key_values` as input, which is the previously computed key/value attention pairs.
  TensorFlow models accepts `past` as input. Using the `past_key_values` value prevents the model from re-computing
  pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward)
  method for more information on the usage of this argument.

This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
[here](https://github.com/salesforce/ctrl).

## Documentation resources

- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)

## CTRLConfig

[[autodoc]] CTRLConfig

## CTRLTokenizer

[[autodoc]] CTRLTokenizer
    - save_vocabulary

## CTRLModel

[[autodoc]] CTRLModel
    - forward

## CTRLLMHeadModel

[[autodoc]] CTRLLMHeadModel
    - forward

## CTRLForSequenceClassification

[[autodoc]] CTRLForSequenceClassification
    - forward

## TFCTRLModel

[[autodoc]] TFCTRLModel
    - call

## TFCTRLLMHeadModel

[[autodoc]] TFCTRLLMHeadModel
    - call

## TFCTRLForSequenceClassification

[[autodoc]] TFCTRLForSequenceClassification
    - call

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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# CTRL

<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>

## Overview

CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).

The abstract from the paper is the following:

*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.*

Tips:

- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
  or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
  more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
  observed in the *run_generation.py* example script.
- The PyTorch models can take the `past_key_values` as input, which is the previously computed key/value attention pairs.
  TensorFlow models accepts `past` as input. Using the `past_key_values` value prevents the model from re-computing
  pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward)
  method for more information on the usage of this argument.

This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
[here](https://github.com/salesforce/ctrl).

## Documentation resources

- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)

## CTRLConfig

[[autodoc]] CTRLConfig

## CTRLTokenizer

[[autodoc]] CTRLTokenizer
    - save_vocabulary

## CTRLModel

[[autodoc]] CTRLModel
    - forward

## CTRLLMHeadModel

[[autodoc]] CTRLLMHeadModel
    - forward

## CTRLForSequenceClassification

[[autodoc]] CTRLForSequenceClassification
    - forward

## TFCTRLModel

[[autodoc]] TFCTRLModel
    - call

## TFCTRLLMHeadModel

[[autodoc]] TFCTRLLMHeadModel
    - call

## TFCTRLForSequenceClassification

[[autodoc]] TFCTRLForSequenceClassification
    - call
