# 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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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [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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 Please tag fewer than 3 people.

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

 -->

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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Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

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

 -->

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rendered properly in your Markdown viewer.

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# BERTología

Hay un creciente campo de estudio empeñado en la investigación del funcionamiento interno de los transformers de gran escala como BERT
(que algunos llaman "BERTología"). Algunos buenos ejemplos de este campo son:


- BERT Rediscovers the Classical NLP Pipeline por Ian Tenney, Dipanjan Das, Ellie Pavlick:
  https://arxiv.org/abs/1905.05950
- Are Sixteen Heads Really Better than One? por Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
- What Does BERT Look At? An Analysis of BERT's Attention por Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
  Manning: https://arxiv.org/abs/1906.04341
- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633

Para asistir al desarrollo de este nuevo campo, hemos incluido algunas features adicionales en los modelos BERT/GPT/GPT-2 para
ayudar a acceder a las representaciones internas, principalmente adaptado de la gran obra de Paul Michel
(https://arxiv.org/abs/1905.10650):


- accediendo a todos los hidden-states de BERT/GPT/GPT-2,
- accediendo a todos los pesos de atención para cada head de BERT/GPT/GPT-2,
- adquiriendo los valores de salida y gradientes de las heads para poder computar la métrica de importancia de las heads y realizar la poda de heads como se explica
  en https://arxiv.org/abs/1905.10650.

Para ayudarte a entender y usar estas features, hemos añadido un script específico de ejemplo: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) mientras extraes información y cortas un modelo pre-entrenado en
GLUE.

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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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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.

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# BERTología

Hay un creciente campo de estudio empeñado en la investigación del funcionamiento interno de los transformers de gran escala como BERT
(que algunos llaman "BERTología"). Algunos buenos ejemplos de este campo son:


- BERT Rediscovers the Classical NLP Pipeline por Ian Tenney, Dipanjan Das, Ellie Pavlick:
  https://arxiv.org/abs/1905.05950
- Are Sixteen Heads Really Better than One? por Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650
- What Does BERT Look At? An Analysis of BERT's Attention por Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
  Manning: https://arxiv.org/abs/1906.04341
- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633

Para asistir al desarrollo de este nuevo campo, hemos incluido algunas features adicionales en los modelos BERT/GPT/GPT-2 para
ayudar a acceder a las representaciones internas, principalmente adaptado de la gran obra de Paul Michel
(https://arxiv.org/abs/1905.10650):


- accediendo a todos los hidden-states de BERT/GPT/GPT-2,
- accediendo a todos los pesos de atención para cada head de BERT/GPT/GPT-2,
- adquiriendo los valores de salida y gradientes de las heads para poder computar la métrica de importancia de las heads y realizar la poda de heads como se explica
  en https://arxiv.org/abs/1905.10650.

Para ayudarte a entender y usar estas features, hemos añadido un script específico de ejemplo: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) mientras extraes información y cortas un modelo pre-entrenado en
GLUE.
