# 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?
- [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.
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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.

<!-- 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

 -->

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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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.
-->

# Question Answering examples

Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/question-answering/run_qa.py).

**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version
of the script.


The following example fine-tunes BERT on SQuAD:


```bash
python run_qa.py \
  --model_name_or_path bert-base-uncased \
  --dataset_name squad \
  --do_train   \
  --do_eval   \
  --max_seq_length 384 \
  --doc_stride 128 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --per_device_train_batch_size 12 \
  --output_dir ./bert-qa-squad \
  --eval_steps 1000 \
  --push_to_hub
```

Using the command above, the script will train for 2 epochs and run eval after each epoch. 
Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`.
You can see the results by running `tensorboard` in that directory:

```bash
$ tensorboard --logdir .
```

or directly on the hub under *Training metrics*.

Training with the previously defined hyper-parameters yields the following results:

```bash
f1 = 88.62
exact_match = 81.34
```

sample Metrics - [tfhub.dev](https://tensorboard.dev/experiment/6gU75Hx8TGCnc6tr4ZgI9Q)

Here is an example training on 4 TITAN RTX GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:

```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
python run_qa.py   \
--model_name_or_path bert-large-uncased-whole-word-masking   \
--dataset_name squad   \
--do_train   \
--do_eval   \
--per_device_train_batch_size 6   \
--learning_rate 3e-5   \
--num_train_epochs 2   \
--max_seq_length 384   \
--doc_stride 128   \
--output_dir ./wwm_uncased_finetuned_squad/ \
--eval_steps 1000 \
--push_to_hub
```

Training with the previously defined hyper-parameters yields the following results:

```bash
f1 = 93.31
exact_match = 87.04
```


### Usage notes

Note that when contexts are long they may be split into multiple training cases, not all of which may contain
the answer span. 

As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
inputs as well.

### Memory usage and data loading

One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming.

<!---
Copyright 2021 The Google Flax Team Authors and HuggingFace Team. All rights reserved.

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.
-->

# Question Answering examples

Based on the script [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/question-answering/run_qa.py).

**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version
of the script.


The following example fine-tunes BERT on SQuAD:


```bash
python run_qa.py \
  --model_name_or_path bert-base-uncased \
  --dataset_name squad \
  --do_train   \
  --do_eval   \
  --max_seq_length 384 \
  --doc_stride 128 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --per_device_train_batch_size 12 \
  --output_dir ./bert-qa-squad \
  --eval_steps 1000 \
  --push_to_hub
```

Using the command above, the script will train for 2 epochs and run eval after each epoch. 
Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`.
You can see the results by running `tensorboard` in that directory:

```bash
$ tensorboard --logdir .
```

or directly on the hub under *Training metrics*.

Training with the previously defined hyper-parameters yields the following results:

```bash
f1 = 88.62
exact_match = 81.34
```

sample Metrics - [tfhub.dev](https://tensorboard.dev/experiment/6gU75Hx8TGCnc6tr4ZgI9Q)

Here is an example training on 4 TITAN RTX GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:

```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
python run_qa.py   \
--model_name_or_path bert-large-uncased-whole-word-masking   \
--dataset_name squad   \
--do_train   \
--do_eval   \
--per_device_train_batch_size 6   \
--learning_rate 3e-5   \
--num_train_epochs 2   \
--max_seq_length 384   \
--doc_stride 128   \
--output_dir ./wwm_uncased_finetuned_squad/ \
--eval_steps 1000 \
--push_to_hub
```

Training with the previously defined hyper-parameters yields the following results:

```bash
f1 = 93.31
exact_match = 87.04
```


### Usage notes

Note that when contexts are long they may be split into multiple training cases, not all of which may contain
the answer span. 

As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
inputs as well.

### Memory usage and data loading

One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming.
