ard 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
    ```Zspeecht5