class_docstr: This is the configuration class to store the configuration of a [`SpeechT5HifiGanModel`]. It is used to instantiate
  a SpeechT5 HiFi-GAN vocoder model according to the specified arguments, defining the model architecture.
  Instantiating a configuration with the defaults will yield a similar configuration to that of the SpeechT5
  [microsoft/speecht5_hifigan](https://huggingface.co/microsoft/speecht5_hifigan) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      model_in_dim (`int`, *optional*, defaults to 80):
          The number of frequency bins in the input log-mel spectrogram.
      sampling_rate (`int`, *optional*, defaults to 16000):
          The sampling rate at which the output audio will be generated, expressed in hertz (Hz).
      upsample_initial_channel (`int`, *optional*, defaults to 512):
          The number of input channels into the upsampling network.
      upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
          A tuple of integers defining the stride of each 1D convolutional layer in the upsampling network. The
          length of *upsample_rates* defines the number of convolutional layers and has to match the length of
          *upsample_kernel_sizes*.
      upsample_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 8, 8]`):
          A tuple of integers defining the kernel size of each 1D convolutional layer in the upsampling network. The
          length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match the length of
          *upsample_rates*.
      resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`):
          A tuple of integers defining the kernel sizes of the 1D convolutional layers in the multi-receptive field
          fusion (MRF) module.
      resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`):
          A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the
          multi-receptive field fusion (MRF) module.
      initializer_range (`float`, *optional*, defaults to 0.01):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      leaky_relu_slope (`float`, *optional*, defaults to 0.1):
          The angle of the negative slope used by the leaky ReLU activation.
      normalize_before (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the spectrogram before vocoding using the vocoder's learned mean and variance.
  
  Example:
  
  ```python
  >>> from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig
  
  >>> # Initializing a "microsoft/speecht5_hifigan" style configuration
  >>> configuration = SpeechT5HifiGanConfig()
  
  >>> # Initializing a model (with random weights) from the "microsoft/speecht5_hifigan" style configuration
  >>> model = SpeechT5HifiGan(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      model_in_dim=80,
      sampling_rate=16000,
      upsample_initial_channel=512,
      upsample_rates=[4, 4, 4, 4],
      upsample_kernel_sizes=[8, 8, 8, 8],
      resblock_kernel_sizes=[3, 7, 11],
      resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
      initializer_range=0.01,
      leaky_relu_slope=0.1,
      normalize_before=True,
      **kwargs,
  ):
      self.model_in_dim = model_in_dim
      self.sampling_rate = sampling_rate
      self.upsample_initial_channel = upsample_initial_channel
      self.upsample_rates = upsample_rates
      self.upsample_kernel_sizes = upsample_kernel_sizes
      self.resblock_kernel_sizes = resblock_kernel_sizes
      self.resblock_dilation_sizes = resblock_dilation_sizes
      self.initializer_range = initializer_range
      self.leaky_relu_slope = leaky_relu_slope
      self.normalize_before = normalize_before
      super().__init__(**kwargs)
  
file_path: transformers/src/transformers/models/speecht5/configuration_speecht5.py
imports: import functools
  import operator
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new SpeechT5HifiGanConfig object from inherited base classes: PretrainedConfig with example: obj = SpeechT5HifiGanConfig(model_in_dim, sampling_rate, upsample_initial_channel, upsample_rates, upsample_kernel_sizes, resblock_kernel_sizes, resblock_dilation_sizes, initializer_range, leaky_relu_slope, normalize_before)