ported.
        feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for quantized feature encoder states.
        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
            feature encoder. 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 feature encoder. 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 feature encoder. 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.
        do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
            Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
            True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
            False` corresponds to applying layer norm after the attention layer.
        apply_spec_augment (`bool`, *optional*, defaults to `True`):
            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. 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_codevectors_per_group (`int`, *optional*, defaults to 320):
            Number of entries in each quantization codebook (group).
        num_codevector_groups (`int`, *optional*, defaults to 2):
            Number of codevector groups for product codevector quantization.
        contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
            The temperature *kappa* in the contrastive loss.
        feat_quantizer_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
        num_negatives (`int`, *optional*, defaults to 100):
            Number of negative samples for the contrastive loss.
        codevector_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the quantized feature vectors.
        proj_codevector_dim (`int`, *optional*, defaults to 256):
            Dimensionality of the final projection of both the quantized and the transformer features.
        diversity_loss_weight (`int`, *optional*, defaults to 0.1):
            The weight of the codebook diversity loss component.
        ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
            Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
            instance of [`UniSpeechForCTC`].
        ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
            Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
            occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
            of [`UniSpeechForCTC`].
        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
            instance of [`UniSpeechForSequenceClassification`].
        classifier_proj_size (`int`, *optional*, defaults to 256):
            Dimensionality of the projection before token mean-pooling for classification.
        replace_prob (`float`, *optional*, defaults to 0.5):
            Propability that transformer feature is replaced by quantized feature for pretraining.

    Example:

    ```python
    >>> from transformers import UniSpeechConfig, UniSpeechModel

    >>> # Initializing a UniSpeech facebook/unispeech-base-960h style configuration
    >>> configuration = UniSpeechConfig()

    >>> # Initializing a model (with random weights) from the facebook/unispeech-base-960h style configuration
    >>> model = UniSpeechModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Z	unispeeché 