


'''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 		_snake_case     (					__lowerCAmelCase		):






						'''simple docstring'''







						__snake_case				=   """microsoft/speecht5_tts"""
						__snake_case				=   (
						    """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."""
						)
						__snake_case				=   """text_reader"""
						__snake_case				=   SpeechTaProcessor
						__snake_case				=   SpeechTaForTextToSpeech
						__snake_case				=   SpeechTaHifiGan

						__snake_case				=   ["""text"""]
						__snake_case				=   ["""audio"""]







						def lowerCAmelCase__			(	self:						Dict  )				->						Union[str, Any]:
										if self.post_processor is None:
														__magic_name__					:       Optional[int]    							=							"microsoft/speecht5_hifigan"
										super().setup()







						def lowerCAmelCase__			(	self:						Union[str, Any]   ,				__UpperCamelCase:						List[Any]   ,				__UpperCamelCase:						Tuple=None  )				->						List[str]:
										__magic_name__					:       Optional[int]    							=							self.pre_processor(text=_SCREAMING_SNAKE_CASE   ,				return_tensors="pt"   ,				truncation=_SCREAMING_SNAKE_CASE  )

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

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

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







						def lowerCAmelCase__			(	self:						Union[str, Any]   ,				__UpperCamelCase:						Optional[Any]  )				->						str:
										with torch.no_grad():
														return self.model.generate_speech(**_SCREAMING_SNAKE_CASE  )







						def lowerCAmelCase__			(	self:						Tuple   ,				__UpperCamelCase:						Any  )				->						Any:
										with torch.no_grad():
														return self.post_processor(_SCREAMING_SNAKE_CASE  ).cpu().detach()





'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_		:     Optional[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Any          =		{
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		"""unispeech-sat"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE="group"   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)   ,_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)   ,_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=128   ,_SCREAMING_SNAKE_CASE=16   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0_5   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=320   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=100   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE="mean"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1_500)   ,_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=504   ,**_SCREAMING_SNAKE_CASE   ,)				->     Any:
          super().__init__(**_SCREAMING_SNAKE_CASE   ,pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size
          _snake_case				= feat_extract_norm
          _snake_case				= feat_extract_activation
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= conv_bias
          _snake_case				= num_conv_pos_embeddings
          _snake_case				= num_conv_pos_embedding_groups
          _snake_case				= len(self.conv_dim       )
          _snake_case				= num_hidden_layers
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= num_attention_heads
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout
          _snake_case				= activation_dropout
          _snake_case				= feat_proj_dropout
          _snake_case				= final_dropout
          _snake_case				= layerdrop
          _snake_case				= layer_norm_eps
          _snake_case				= initializer_range
          _snake_case				= vocab_size
          _snake_case				= num_clusters
          _snake_case				= do_stable_layer_norm
          _snake_case				= use_weighted_layer_sum

          if (
              (len(self.conv_stride       ) != self.num_feat_extract_layers)
              or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
              or (len(self.conv_dim       ) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                   " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                   f""" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"""
                   f""" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."""       )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _snake_case				= apply_spec_augment
          _snake_case				= mask_time_prob
          _snake_case				= mask_time_length
          _snake_case				= mask_time_min_masks
          _snake_case				= mask_feature_prob
          _snake_case				= mask_feature_length
          _snake_case				= mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          _snake_case				= num_codevectors_per_group
          _snake_case				= num_codevector_groups
          _snake_case				= contrastive_logits_temperature
          _snake_case				= feat_quantizer_dropout
          _snake_case				= num_negatives
          _snake_case				= codevector_dim
          _snake_case				= proj_codevector_dim
          _snake_case				= diversity_loss_weight

          # ctc loss
          _snake_case				= ctc_loss_reduction
          _snake_case				= ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          _snake_case				= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= xvector_output_dim






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          return functools.reduce(operator.mul   ,self.conv_stride   ,1       )


