# 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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Copyright 2020 The 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

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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.
-->

## Translation

This directory contains examples for finetuning and evaluating transformers on translation tasks.
Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR!
For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md).
For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/main/examples/legacy/seq2seq).

### Supported Architectures

- `BartForConditionalGeneration`
- `FSMTForConditionalGeneration` (translation only)
- `MBartForConditionalGeneration`
- `MarianMTModel`
- `PegasusForConditionalGeneration`
- `T5ForConditionalGeneration`
- `MT5ForConditionalGeneration`

`run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.

For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files
and you also will find examples of these below.


## With Trainer

Here is an example of a translation fine-tuning with a MarianMT model:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

MBart and some T5 models require special handling.

T5 models `t5-small`, `t5-base`, `t5-large`, `t5-3b` and `t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --source_prefix "translate English to Romanian: " \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

If you get a terrible BLEU score, make sure that you didn't forget to use the `--source_prefix` argument.

For the aforementioned group of T5 models it's important to remember that if you switch to a different language pair, make sure to adjust the source and target values in all 3 language-specific command line argument: `--source_lang`, `--target_lang` and `--source_prefix`.

MBart models require a different format for `--source_lang` and `--target_lang` values, e.g. instead of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be found [here](https://huggingface.co/facebook/mbart-large-cc25). For example:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path facebook/mbart-large-en-ro  \
    --do_train \
    --do_eval \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --source_lang en_XX \
    --target_lang ro_RO \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
 ```

And here is how you would use the translation finetuning on your own files, after adjusting the
values for the arguments `--train_file`, `--validation_file` to match your setup:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --source_prefix "translate English to Romanian: " \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --train_file path_to_jsonlines_file \
    --validation_file path_to_jsonlines_file \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

The task of translation supports only custom JSONLINES files, with each line being a dictionary with a key `"translation"` and its value another dictionary whose keys is the language pair. For example:

```json
{ "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit o glumă." } }
{ "translation": { "en": "And some are holding out for an implosion.", "ro": "Iar alții așteaptă implozia." } }
```
Here the languages are Romanian (`ro`) and English (`en`).

If you want to use a pre-processed dataset that leads to high BLEU scores, but for the `en-de` language pair, you can use `--dataset_name stas/wmt14-en-de-pre-processed`, as following:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang de \
    --source_prefix "translate English to German: " \
    --dataset_name stas/wmt14-en-de-pre-processed \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
 ```

## With Accelerate

Based on the script [`run_translation_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py).

Like `run_translation.py`, this script allows you to fine-tune any of the models supported on a
translation task, the main difference is that this
script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.

It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
after installing it:

```bash
pip install git+https://github.com/huggingface/accelerate
```

then

```bash
python run_translation_no_trainer.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir ~/tmp/tst-translation
```

You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run

```bash
accelerate config
```

and reply to the questions asked. Then

```bash
accelerate test
```

that will check everything is ready for training. Finally, you can launch training with

```bash
accelerate launch run_translation_no_trainer.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir ~/tmp/tst-translation
```

This command is the same and will work for:

- a CPU-only setup
- a setup with one GPU
- a distributed training with several GPUs (single or multi node)
- a training on TPUs

Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.

<!---
Copyright 2020 The 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.
-->

## Translation

This directory contains examples for finetuning and evaluating transformers on translation tasks.
Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR!
For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md).
For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/main/examples/legacy/seq2seq).

### Supported Architectures

- `BartForConditionalGeneration`
- `FSMTForConditionalGeneration` (translation only)
- `MBartForConditionalGeneration`
- `MarianMTModel`
- `PegasusForConditionalGeneration`
- `T5ForConditionalGeneration`
- `MT5ForConditionalGeneration`

`run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.

For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets.html#json-files
and you also will find examples of these below.


## With Trainer

Here is an example of a translation fine-tuning with a MarianMT model:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

MBart and some T5 models require special handling.

T5 models `t5-small`, `t5-base`, `t5-large`, `t5-3b` and `t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --source_prefix "translate English to Romanian: " \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

If you get a terrible BLEU score, make sure that you didn't forget to use the `--source_prefix` argument.

For the aforementioned group of T5 models it's important to remember that if you switch to a different language pair, make sure to adjust the source and target values in all 3 language-specific command line argument: `--source_lang`, `--target_lang` and `--source_prefix`.

MBart models require a different format for `--source_lang` and `--target_lang` values, e.g. instead of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be found [here](https://huggingface.co/facebook/mbart-large-cc25). For example:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path facebook/mbart-large-en-ro  \
    --do_train \
    --do_eval \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --source_lang en_XX \
    --target_lang ro_RO \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
 ```

And here is how you would use the translation finetuning on your own files, after adjusting the
values for the arguments `--train_file`, `--validation_file` to match your setup:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang ro \
    --source_prefix "translate English to Romanian: " \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --train_file path_to_jsonlines_file \
    --validation_file path_to_jsonlines_file \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
```

The task of translation supports only custom JSONLINES files, with each line being a dictionary with a key `"translation"` and its value another dictionary whose keys is the language pair. For example:

```json
{ "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit o glumă." } }
{ "translation": { "en": "And some are holding out for an implosion.", "ro": "Iar alții așteaptă implozia." } }
```
Here the languages are Romanian (`ro`) and English (`en`).

If you want to use a pre-processed dataset that leads to high BLEU scores, but for the `en-de` language pair, you can use `--dataset_name stas/wmt14-en-de-pre-processed`, as following:

```bash
python examples/pytorch/translation/run_translation.py \
    --model_name_or_path t5-small \
    --do_train \
    --do_eval \
    --source_lang en \
    --target_lang de \
    --source_prefix "translate English to German: " \
    --dataset_name stas/wmt14-en-de-pre-processed \
    --output_dir /tmp/tst-translation \
    --per_device_train_batch_size=4 \
    --per_device_eval_batch_size=4 \
    --overwrite_output_dir \
    --predict_with_generate
 ```

## With Accelerate

Based on the script [`run_translation_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py).

Like `run_translation.py`, this script allows you to fine-tune any of the models supported on a
translation task, the main difference is that this
script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.

It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
after installing it:

```bash
pip install git+https://github.com/huggingface/accelerate
```

then

```bash
python run_translation_no_trainer.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir ~/tmp/tst-translation
```

You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run

```bash
accelerate config
```

and reply to the questions asked. Then

```bash
accelerate test
```

that will check everything is ready for training. Finally, you can launch training with

```bash
accelerate launch run_translation_no_trainer.py \
    --model_name_or_path Helsinki-NLP/opus-mt-en-ro \
    --source_lang en \
    --target_lang ro \
    --dataset_name wmt16 \
    --dataset_config_name ro-en \
    --output_dir ~/tmp/tst-translation
```

This command is the same and will work for:

- a CPU-only setup
- a setup with one GPU
- a distributed training with several GPUs (single or multi node)
- a training on TPUs

Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
