class_docstr: This is the configuration class to store the configuration of a [`SpeechT5Model`]. It is used to instantiate a
  SpeechT5 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_asr](https://huggingface.co/microsoft/speecht5_asr) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 81):
          Vocabulary size of the SpeechT5 model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed to the forward method of [`SpeechT5Model`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      encoder_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      encoder_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      encoder_layerdrop (`float`, *optional*, defaults to 0.1):
          The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_layers (`int`, *optional*, defaults to 6):
          Number of hidden layers in the Transformer decoder.
      decoder_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer decoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer decoder.
      decoder_layerdrop (`float`, *optional*, defaults to 0.1):
          The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` are supported.
      positional_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for the text position encoding layers.
      hidden_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.1):
          The dropout ratio for activations inside the fully connected layer.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-5):
          The epsilon used by the layer normalization layers.
      scale_embedding (`bool`, *optional*, defaults to `False`):
          Scale embeddings by diving by sqrt(d_model).
      feat_extract_norm (`str`, *optional*, defaults to `"group"`):
          The norm to be applied to 1D convolutional layers in the speech encoder pre-net. One of `"group"` for group
          normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
          convolutional layers.
      feat_proj_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for output of the speech encoder pre-net.
      feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the 1D convolutional layers of the feature
          extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
      conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
          A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
          speech encoder pre-net. The length of *conv_dim* defines the number of 1D convolutional layers.
      conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
          A tuple of integers defining the stride of each 1D convolutional layer in the speech encoder pre-net. The
          length of *conv_stride* defines the number of convolutional layers and has to match the length of
          *conv_dim*.
      conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
          A tuple of integers defining the kernel size of each 1D convolutional layer in the speech encoder pre-net.
          The length of *conv_kernel* defines the number of convolutional layers and has to match the length of
          *conv_dim*.
      conv_bias (`bool`, *optional*, defaults to `False`):
          Whether the 1D convolutional layers have a bias.
      num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
          Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
          embeddings layer.
      num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
          Number of groups of 1D convolutional positional embeddings layer.
      apply_spec_augment (`bool`, *optional*, defaults to `True`):
          Whether to apply *SpecAugment* data augmentation to the outputs of the speech encoder pre-net. For
          reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
          Recognition](https://arxiv.org/abs/1904.08779).
      mask_time_prob (`float`, *optional*, defaults to 0.05):
          Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
          procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
          reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
          masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
          actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
      mask_time_length (`int`, *optional*, defaults to 10):
          Length of vector span along the time axis.
      mask_time_min_masks (`int`, *optional*, defaults to 2),:
          The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
          irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
          mask_time_min_masks''
      mask_feature_prob (`float`, *optional*, defaults to 0.0):
          Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
          masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
          the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
          span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
          may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
          True`.
      mask_feature_length (`int`, *optional*, defaults to 10):
          Length of vector span along the feature axis.
      mask_feature_min_masks (`int`, *optional*, defaults to 0),:
          The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
          step, irrespectively of `mask_feature_prob`. Only relevant if
          ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
      num_mel_bins (`int`, *optional*, defaults to 80):
          Number of mel features used per input features. Used by the speech decoder pre-net. Should correspond to
          the value used in the [`SpeechT5Processor`] class.
      speech_decoder_prenet_layers (`int`, *optional*, defaults to 2):
          Number of layers in the speech decoder pre-net.
      speech_decoder_prenet_units (`int`, *optional*, defaults to 256):
          Dimensionality of the layers in the speech decoder pre-net.
      speech_decoder_prenet_dropout (`float`, *optional*, defaults to 0.5):
          The dropout probability for the speech decoder pre-net layers.
      speaker_embedding_dim (`int`, *optional*, defaults to 512):
          Dimensionality of the *XVector* embedding vectors.
      speech_decoder_postnet_layers (`int`, *optional*, defaults to 5):
          Number of layers in the speech decoder post-net.
      speech_decoder_postnet_units (`int`, *optional*, defaults to 256):
          Dimensionality of the layers in the speech decoder post-net.
      speech_decoder_postnet_kernel (`int`, *optional*, defaults to 5):
          Number of convolutional filter channels in the speech decoder post-net.
      speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5):
          The dropout probability for the speech decoder post-net layers.
      reduction_factor (`int`, *optional*, defaults to 2):
          Spectrogram length reduction factor for the speech decoder inputs.
      max_speech_positions (`int`, *optional*, defaults to 4000):
          The maximum sequence length of speech features that this model might ever be used with.
      max_text_positions (`int`, *optional*, defaults to 450):
          The maximum sequence length of text features that this model might ever be used with.
      encoder_max_relative_position (`int`, *optional*, defaults to 160):
          Maximum distance for relative position embedding in the encoder.
      use_guided_attention_loss (`bool`, *optional*, defaults to `True`):
          Whether to apply guided attention loss while training the TTS model.
      guided_attention_loss_num_heads (`int`, *optional*, defaults to 2):
          Number of attention heads the guided attention loss will be applied to. Use -1 to apply this loss to all
          attention heads.
      guided_attention_loss_sigma (`float`, *optional*, defaults to 0.4):
          Standard deviation for guided attention loss.
      guided_attention_loss_scale (`float`, *optional*, defaults to 10.0):
          Scaling coefficient for guided attention loss (also known as lambda).
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
  
  Example:
  
  ```python
  >>> from transformers import SpeechT5Model, SpeechT5Config
  
  >>> # Initializing a "microsoft/speecht5_asr" style configuration
  >>> configuration = SpeechT5Config()
  
  >>> # Initializing a model (with random weights) from the "microsoft/speecht5_asr" style configuration
  >>> model = SpeechT5Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=81,
      hidden_size=768,
      encoder_layers=12,
      encoder_attention_heads=12,
      encoder_ffn_dim=3072,
      encoder_layerdrop=0.1,
      decoder_layers=6,
      decoder_ffn_dim=3072,
      decoder_attention_heads=12,
      decoder_layerdrop=0.1,
      hidden_act="gelu",
      positional_dropout=0.1,
      hidden_dropout=0.1,
      attention_dropout=0.1,
      activation_dropout=0.1,
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      scale_embedding=False,
      feat_extract_norm="group",
      feat_proj_dropout=0.0,
      feat_extract_activation="gelu",
      conv_dim=(512, 512, 512, 512, 512, 512, 512),
      conv_stride=(5, 2, 2, 2, 2, 2, 2),
      conv_kernel=(10, 3, 3, 3, 3, 2, 2),
      conv_bias=False,
      num_conv_pos_embeddings=128,
      num_conv_pos_embedding_groups=16,
      apply_spec_augment=True,
      mask_time_prob=0.05,
      mask_time_length=10,
      mask_time_min_masks=2,
      mask_feature_prob=0.0,
      mask_feature_length=10,
      mask_feature_min_masks=0,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      decoder_start_token_id=2,
      num_mel_bins=80,
      speech_decoder_prenet_layers=2,
      speech_decoder_prenet_units=256,
      speech_decoder_prenet_dropout=0.5,
      speaker_embedding_dim=512,
      speech_decoder_postnet_layers=5,
      speech_decoder_postnet_units=256,
      speech_decoder_postnet_kernel=5,
      speech_decoder_postnet_dropout=0.5,
      reduction_factor=2,
      max_speech_positions=4000,
      max_text_positions=450,
      encoder_max_relative_position=160,
      use_guided_attention_loss=True,
      guided_attention_loss_num_heads=2,
      guided_attention_loss_sigma=0.4,
      guided_attention_loss_scale=10.0,
      use_cache=True,
      is_encoder_decoder=True,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.encoder_layers = encoder_layers
      self.encoder_ffn_dim = encoder_ffn_dim
      self.encoder_attention_heads = encoder_attention_heads
      self.encoder_layerdrop = encoder_layerdrop
      self.decoder_layers = decoder_layers
      self.decoder_ffn_dim = decoder_ffn_dim
      self.decoder_attention_heads = decoder_attention_heads
      self.decoder_layerdrop = decoder_layerdrop
      self.hidden_act = hidden_act
      self.positional_dropout = positional_dropout
      self.hidden_dropout = hidden_dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.scale_embedding = scale_embedding
  
      self.feat_extract_norm = feat_extract_norm
      self.feat_proj_dropout = feat_proj_dropout
      self.feat_extract_activation = feat_extract_activation
      self.conv_dim = list(conv_dim)
      self.conv_stride = list(conv_stride)
      self.conv_kernel = list(conv_kernel)
      self.conv_bias = conv_bias
      self.num_conv_pos_embeddings = num_conv_pos_embeddings
      self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
      self.num_feat_extract_layers = len(self.conv_dim)
  
      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
      self.apply_spec_augment = apply_spec_augment
      self.mask_time_prob = mask_time_prob
      self.mask_time_length = mask_time_length
      self.mask_time_min_masks = mask_time_min_masks
      self.mask_feature_prob = mask_feature_prob
      self.mask_feature_length = mask_feature_length
      self.mask_feature_min_masks = mask_feature_min_masks
  
      self.num_mel_bins = num_mel_bins
      self.speech_decoder_prenet_layers = speech_decoder_prenet_layers
      self.speech_decoder_prenet_units = speech_decoder_prenet_units
      self.speech_decoder_prenet_dropout = speech_decoder_prenet_dropout
      self.speaker_embedding_dim = speaker_embedding_dim
  
      self.speech_decoder_postnet_layers = speech_decoder_postnet_layers
      self.speech_decoder_postnet_units = speech_decoder_postnet_units
      self.speech_decoder_postnet_kernel = speech_decoder_postnet_kernel
      self.speech_decoder_postnet_dropout = (
          speech_decoder_postnet_dropout
      )
      self.reduction_factor = reduction_factor
  
      self.max_speech_positions = max_speech_positions
      self.max_text_positions = max_text_positions
      self.encoder_max_relative_position = encoder_max_relative_position
  
      self.use_guided_attention_loss = use_guided_attention_loss
      self.guided_attention_loss_num_heads = (
          guided_attention_loss_num_heads
      )
      self.guided_attention_loss_sigma = guided_attention_loss_sigma
      self.guided_attention_loss_scale = guided_attention_loss_scale
  
      self.use_cache = use_cache
      self.is_encoder_decoder = is_encoder_decoder
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          decoder_start_token_id=decoder_start_token_id,
          **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 SpeechT5Config object from inherited base classes: PretrainedConfig with example: obj = SpeechT5Config(vocab_size, hidden_size, encoder_layers, encoder_attention_heads, encoder_ffn_dim, encoder_layerdrop, decoder_layers, decoder_ffn_dim, decoder_attention_heads, decoder_layerdrop, hidden_act, positional_dropout, hidden_dropout, attention_dropout, activation_dropout, initializer_range, layer_norm_eps, scale_embedding, feat_extract_norm, feat_proj_dropout, feat_extract_activation, conv_dim, conv_stride, conv_kernel, conv_bias, num_conv_pos_embeddings, num_conv_pos_embedding_groups, apply_spec_augment, mask_time_prob, mask_time_length, mask_time_min_masks, mask_feature_prob, mask_feature_length, mask_feature_min_masks, pad_token_id, bos_token_id, eos_token_id, decoder_start_token_id, num_mel_bins, speech_decoder_prenet_layers, speech_decoder_prenet_units, speech_decoder_prenet_dropout, speaker_embedding_dim, speech_decoder_postnet_layers, speech_decoder_postnet_units, speech_decoder_postnet_kernel, speech_decoder_postnet_dropout, reduction_factor, max_speech_positions, max_text_positions, encoder_max_relative_position, use_guided_attention_loss, guided_attention_loss_num_heads, guided_attention_loss_sigma, guided_attention_loss_scale, use_cache, is_encoder_decoder)