# 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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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

 -->

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).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

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.
-->

<!-- 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

 -->

#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 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.
"""
Create a VisionEncoderDecoderModel instance from pretrained encoder/decoder models.

The cross-attention will be randomly initialized.
"""

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    output_dir: str = field(
        metadata={"help": "The output directory where the model will be written."},
    )
    encoder_model_name_or_path: str = field(
        metadata={
            "help": (
                "The encoder model checkpoint for weights initialization."
                "Don't set if you want to train an encoder model from scratch."
            )
        },
    )
    decoder_model_name_or_path: str = field(
        metadata={
            "help": (
                "The decoder model checkpoint for weights initialization."
                "Don't set if you want to train a decoder model from scratch."
            )
        },
    )
    encoder_config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}
    )
    decoder_config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}
    )


def main():
    parser = HfArgumentParser((ModelArguments,))
    (model_args,) = parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

    # Use explicit specified encoder config
    if model_args.encoder_config_name:
        encoder_config = AutoConfig.from_pretrained(model_args.encoder_config_name)
    # Use pretrained encoder model's config
    else:
        encoder_config = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

    # Use explicit specified decoder config
    if model_args.decoder_config_name:
        decoder_config = AutoConfig.from_pretrained(model_args.decoder_config_name)
    # Use pretrained decoder model's config
    else:
        decoder_config = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

    # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
    decoder_config.is_decoder = True
    decoder_config.add_cross_attention = True

    model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
        encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,
        decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,
        encoder_config=encoder_config,
        decoder_config=decoder_config,
    )

    # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
    decoder_start_token_id = decoder_config.decoder_start_token_id
    pad_token_id = decoder_config.pad_token_id
    if decoder_start_token_id is None:
        decoder_start_token_id = decoder_config.bos_token_id
    if pad_token_id is None:
        pad_token_id = decoder_config.eos_token_id

    # This is necessary to make Flax's generate() work
    model.config.eos_token_id = decoder_config.eos_token_id
    model.config.decoder_start_token_id = decoder_start_token_id
    model.config.pad_token_id = pad_token_id

    image_processor = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

    tokenizer = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
    tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)

    model.save_pretrained(model_args.output_dir)
    image_processor.save_pretrained(model_args.output_dir)
    tokenizer.save_pretrained(model_args.output_dir)


if __name__ == "__main__":
    main()

#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 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.
"""
Create a VisionEncoderDecoderModel instance from pretrained encoder/decoder models.

The cross-attention will be randomly initialized.
"""

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    output_dir: str = field(
        metadata={"help": "The output directory where the model will be written."},
    )
    encoder_model_name_or_path: str = field(
        metadata={
            "help": (
                "The encoder model checkpoint for weights initialization."
                "Don't set if you want to train an encoder model from scratch."
            )
        },
    )
    decoder_model_name_or_path: str = field(
        metadata={
            "help": (
                "The decoder model checkpoint for weights initialization."
                "Don't set if you want to train a decoder model from scratch."
            )
        },
    )
    encoder_config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}
    )
    decoder_config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}
    )


def main():
    parser = HfArgumentParser((ModelArguments,))
    (model_args,) = parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

    # Use explicit specified encoder config
    if model_args.encoder_config_name:
        encoder_config = AutoConfig.from_pretrained(model_args.encoder_config_name)
    # Use pretrained encoder model's config
    else:
        encoder_config = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

    # Use explicit specified decoder config
    if model_args.decoder_config_name:
        decoder_config = AutoConfig.from_pretrained(model_args.decoder_config_name)
    # Use pretrained decoder model's config
    else:
        decoder_config = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

    # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
    decoder_config.is_decoder = True
    decoder_config.add_cross_attention = True

    model = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
        encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,
        decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,
        encoder_config=encoder_config,
        decoder_config=decoder_config,
    )

    # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
    decoder_start_token_id = decoder_config.decoder_start_token_id
    pad_token_id = decoder_config.pad_token_id
    if decoder_start_token_id is None:
        decoder_start_token_id = decoder_config.bos_token_id
    if pad_token_id is None:
        pad_token_id = decoder_config.eos_token_id

    # This is necessary to make Flax's generate() work
    model.config.eos_token_id = decoder_config.eos_token_id
    model.config.decoder_start_token_id = decoder_start_token_id
    model.config.pad_token_id = pad_token_id

    image_processor = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

    tokenizer = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
    tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)

    model.save_pretrained(model_args.output_dir)
    image_processor.save_pretrained(model_args.output_dir)
    tokenizer.save_pretrained(model_args.output_dir)


if __name__ == "__main__":
    main()
