





'''simple docstring'''






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 					UpperCAmelCase	(     _lowercase  ):
      _lowercase:     str			=  '''microsoft/speecht5_tts'''
      _lowercase:     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.'''
      )
      _lowercase:     Any			=  '''text_reader'''
      _lowercase:     List[str]			=  SpeechTaProcessor
      _lowercase:     Union[str, Any]			=  SpeechTaForTextToSpeech
      _lowercase:     List[str]			=  SpeechTaHifiGan

      _lowercase:     Union[str, Any]			=  ['''text''']
      _lowercase:     Union[str, Any]			=  ['''audio''']




      def 							lowercase__       (     self     :					Tuple    )							-> Optional[Any]:
            if self.post_processor is None:
                  _lowerCAmelCase					=				"""microsoft/speecht5_hifigan"""
            super().setup()




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					List[str]     ,     __snake_case     :					Optional[int]=None    )							-> int:
            _lowerCAmelCase					=				self.pre_processor(text=_snake_case     ,     return_tensors="""pt"""     ,     truncation=_snake_case    )

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

                  _lowerCAmelCase					=				load_dataset("""Matthijs/cmu-arctic-xvectors"""     ,     split="""validation"""    )
                  _lowerCAmelCase					=				torch.tensor(embeddings_dataset[73_05]["""xvector"""]    ).unsqueeze(0    )

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




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					Tuple    )							-> Dict:
            with torch.no_grad():
                  return self.model.generate_speech(**_snake_case    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Optional[Any]    )							-> str:
            with torch.no_grad():
                  return self.post_processor(_snake_case    ).cpu().detach()








"""simple docstring"""





from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCamelCase_       :    Dict						      =			{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
		from .config import (
		    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
		    OnnxConfig,
		    OnnxConfigWithPast,
		    OnnxSeqaSeqConfigWithPast,
		    PatchingSpec,
		)
		from .convert import export, validate_model_outputs
		from .features import FeaturesManager
		from .utils import ParameterFormat, compute_serialized_parameters_size

else:
		import sys

		UpperCamelCase_       :    List[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




