


"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. 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.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							Union[str, Any]	=       """microsoft/speecht5_tts"""
    UpperCAmelCase       :							Optional[Any]	=       (
        """This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
        """text to read (in English) and returns a waveform object containing the sound."""
    )
    UpperCAmelCase       :							str	=       """text_reader"""
    UpperCAmelCase       :							str	=       SpeechTaProcessor
    UpperCAmelCase       :							Tuple	=       SpeechTaForTextToSpeech
    UpperCAmelCase       :							Tuple	=       SpeechTaHifiGan

    UpperCAmelCase       :							Optional[Any]	=       ["""text"""]
    UpperCAmelCase       :							List[Any]	=       ["""audio"""]






    def 	__snake_case							(					self				:    Tuple):
      if self.post_processor is None:
        a						:		Tuple      							=			"microsoft/speecht5_hifigan"
      super().setup()






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    List[Any]=None):
      a						:		Any      							=			self.pre_processor(text=__UpperCAmelCase				, return_tensors="pt"				, truncation=__UpperCAmelCase)

      if speaker_embeddings is None:
        if not is_datasets_available():
          raise ImportError("Datasets needs to be installed if not passing speaker embeddings.")

        a						:		List[str]      							=			load_dataset("Matthijs/cmu-arctic-xvectors"				, split="validation")
        a						:		Optional[int]      							=			torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)

      return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Optional[int]):
      with torch.no_grad():
        return self.model.generate_speech(**__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    str):
      with torch.no_grad():
        return self.post_processor(__UpperCAmelCase).cpu().detach()














"""simple docstring"""



__A		:       Any  				=		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}



