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.
# 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.

from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def download_command_factory(args):
    return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code)


class DownloadCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        download_parser = parser.add_parser("download")
        download_parser.add_argument(
            "--cache-dir", type=str, default=None, help="Path to location to store the models"
        )
        download_parser.add_argument(
            "--force", action="store_true", help="Force the model to be download even if already in cache-dir"
        )
        download_parser.add_argument(
            "--trust-remote-code",
            action="store_true",
            help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine",
        )
        download_parser.add_argument("model", type=str, help="Name of the model to download")
        download_parser.set_defaults(func=download_command_factory)

    def __init__(self, model: str, cache: str, force: bool, trust_remote_code: bool):
        self._model = model
        self._cache = cache
        self._force = force
        self._trust_remote_code = trust_remote_code

    def run(self):
        from ..models.auto import AutoModel, AutoTokenizer

        AutoModel.from_pretrained(
            self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code
        )
        AutoTokenizer.from_pretrained(
            self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code
        )

# 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.

from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def download_command_factory(args):
    return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code)


class DownloadCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        download_parser = parser.add_parser("download")
        download_parser.add_argument(
            "--cache-dir", type=str, default=None, help="Path to location to store the models"
        )
        download_parser.add_argument(
            "--force", action="store_true", help="Force the model to be download even if already in cache-dir"
        )
        download_parser.add_argument(
            "--trust-remote-code",
            action="store_true",
            help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine",
        )
        download_parser.add_argument("model", type=str, help="Name of the model to download")
        download_parser.set_defaults(func=download_command_factory)

    def __init__(self, model: str, cache: str, force: bool, trust_remote_code: bool):
        self._model = model
        self._cache = cache
        self._force = force
        self._trust_remote_code = trust_remote_code

    def run(self):
        from ..models.auto import AutoModel, AutoTokenizer

        AutoModel.from_pretrained(
            self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code
        )
        AutoTokenizer.from_pretrained(
            self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code
        )
