






'''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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							int					     =					"microsoft/speecht5_tts"
			__a							:							int					     =					(
			    "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."
			)
			__a							:							Optional[Any]					     =					"text_reader"
			__a							:							Tuple					     =					SpeechTaProcessor
			__a							:							int					     =					SpeechTaForTextToSpeech
			__a							:							int					     =					SpeechTaHifiGan

			__a							:							Optional[int]					     =					["text"]
			__a							:							Union[str, Any]					     =					["audio"]






			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if self.post_processor is None:
															UpperCamelCase__         = """microsoft/speecht5_hifigan"""
									super().setup()






			def 				A      (   self							:						List[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any]=None			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = self.pre_processor(text=lowercase ,      return_tensors="""pt""" ,      truncation=lowercase			)

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

															UpperCamelCase__         = load_dataset("""Matthijs/cmu-arctic-xvectors""" ,      split="""validation"""			)
															UpperCamelCase__         = torch.tensor(embeddings_dataset[7_3_0_5]["""xvector"""]			).unsqueeze(0			)

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






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







									with torch.no_grad():
															return self.model.generate_speech(**lowercase			)







			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									with torch.no_grad():
															return self.post_processor(lowercase			).cpu().detach()














'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE	)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							str					     =					field(default="audio-classification"       ,metadata={"include_in_asdict_even_if_is_default": True}	)
			__a							:							ClassVar[Features]					     =					Features({"audio": Audio()}	)
			__a							:							ClassVar[Features]					     =					Features({"labels": ClassLabel}	)
			__a							:							str					     =					"audio"
			__a							:							str					     =					"labels"






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any]			)							->    Any:

									'''simple docstring'''







									if self.label_column not in features:
															raise ValueError(f"Column {self.label_column} is not present in features."			)
									if not isinstance(features[self.label_column] ,      lowercase			):
															raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
									UpperCamelCase__         = copy.deepcopy(self			)
									UpperCamelCase__         = self.label_schema.copy()
									UpperCamelCase__         = features[self.label_column]
									UpperCamelCase__         = label_schema
									return task_template







			@property
			def 				A      (   self							:						int			)							->    Dict[str, str]:

									'''simple docstring'''







									return {
									    self.audio_column: "audio",
									    self.label_column: "labels",
									}
