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.
# Simple VQGAN CLIP

Author: @ErwannMillon 

This is a very simple VQGAN-CLIP implementation that was built as a part of the <a href= "https://github.com/ErwannMillon/face-editor"> Face Editor project </a> . This simplified version allows you to generate or edit images using text with just three lines of code. For a more full featured implementation with masking, more advanced losses, and a full GUI, check out the Face Editor project. 

By default this uses a CelebA checkpoint (for generating/editing faces), but also has an imagenet checkpoint that can be loaded by specifying vqgan_config and vqgan_checkpoint when instantiating VQGAN_CLIP. 

Learning rate and iterations can be set by modifying vqgan_clip.lr and vqgan_clip.iterations . 

You can edit images by passing `image_path` to the generate function. 
See the generate function's docstring to learn more about how to format prompts. 

## Usage
The easiest way to test this out is by <a href="https://colab.research.google.com/drive/1Ez4D1J6-hVkmlXeR5jBPWYyu6CLA9Yor?usp=sharing
">using the Colab demo</a>

To install locally: 
- Clone this repo
- Install git-lfs (ubuntu: sudo apt-get install git-lfs , MacOS: brew install git-lfs) 

In the root of the repo run:

```
conda create -n vqganclip python=3.8
conda activate vqganclip
git-lfs install
git clone https://huggingface.co/datasets/erwann/face_editor_model_ckpt model_checkpoints
pip install -r requirements.txt
```

### Generate new images
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()
vqgan_clip.generate("a picture of a smiling woman")
```

### Edit an image
To get a test image, run 
`git clone https://huggingface.co/datasets/erwann/vqgan-clip-pic test_images`

To edit:
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()

vqgan_clip.lr = .07
vqgan_clip.iterations = 15
vqgan_clip.generate(
    pos_prompts= ["a picture of a beautiful asian woman", "a picture of a woman from Japan"],
    neg_prompts=["a picture of an Indian person", "a picture of a white person"],
    image_path="./test_images/face.jpeg",
    show_intermediate=True,
    save_intermediate=True,
)
```

### Make an animation from the most recent generation
`vqgan_clip.make_animation()`

## Features:
- Positive and negative prompts
- Multiple prompts
- Prompt Weights
- Creating GIF animations of the transformations
- Wandb logging




# Simple VQGAN CLIP

Author: @ErwannMillon 

This is a very simple VQGAN-CLIP implementation that was built as a part of the <a href= "https://github.com/ErwannMillon/face-editor"> Face Editor project </a> . This simplified version allows you to generate or edit images using text with just three lines of code. For a more full featured implementation with masking, more advanced losses, and a full GUI, check out the Face Editor project. 

By default this uses a CelebA checkpoint (for generating/editing faces), but also has an imagenet checkpoint that can be loaded by specifying vqgan_config and vqgan_checkpoint when instantiating VQGAN_CLIP. 

Learning rate and iterations can be set by modifying vqgan_clip.lr and vqgan_clip.iterations . 

You can edit images by passing `image_path` to the generate function. 
See the generate function's docstring to learn more about how to format prompts. 

## Usage
The easiest way to test this out is by <a href="https://colab.research.google.com/drive/1Ez4D1J6-hVkmlXeR5jBPWYyu6CLA9Yor?usp=sharing
">using the Colab demo</a>

To install locally: 
- Clone this repo
- Install git-lfs (ubuntu: sudo apt-get install git-lfs , MacOS: brew install git-lfs) 

In the root of the repo run:

```
conda create -n vqganclip python=3.8
conda activate vqganclip
git-lfs install
git clone https://huggingface.co/datasets/erwann/face_editor_model_ckpt model_checkpoints
pip install -r requirements.txt
```

### Generate new images
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()
vqgan_clip.generate("a picture of a smiling woman")
```

### Edit an image
To get a test image, run 
`git clone https://huggingface.co/datasets/erwann/vqgan-clip-pic test_images`

To edit:
```
from VQGAN_CLIP import VQGAN_CLIP
vqgan_clip = VQGAN_CLIP()

vqgan_clip.lr = .07
vqgan_clip.iterations = 15
vqgan_clip.generate(
    pos_prompts= ["a picture of a beautiful asian woman", "a picture of a woman from Japan"],
    neg_prompts=["a picture of an Indian person", "a picture of a white person"],
    image_path="./test_images/face.jpeg",
    show_intermediate=True,
    save_intermediate=True,
)
```

### Make an animation from the most recent generation
`vqgan_clip.make_animation()`

## Features:
- Positive and negative prompts
- Multiple prompts
- Prompt Weights
- Creating GIF animations of the transformations
- Wandb logging



