Number of convolution layers before applying transformer encoder layers.
        conv_kernel (`List[int]`, *optional*, defaults to `[7]`):
            The kernel size of the 1D convolution applied before transformer layers. `len(conv_kernel)` must be equal
            to `num_conv_layers`.
        conv_stride (`List[int]`, *optional*, defaults to `[3]`):
            The stride length of the 1D convolution applied before transformer layers. `len(conv_stride)` must be equal
            to `num_conv_layers`.
        input_feat_per_channel (`int`, *optional*, defaults to 80):
            Feature dimensions of the channels of the input to the Conv1D layer.
        input_channels (`int`, *optional*, defaults to 1):
            Number of input channels of the input to the Conv1D layer.
        conv_channels (`List[int]`, *optional*, defaults to None):
            Channel sizes of intermediate Conv1D layers.
        ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
            Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
            instance of [`MCTCTForCTC`].
        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 [`MCTCTForCTC`].

    Example:

    ```python
    >>> from transformers import MCTCTConfig, MCTCTModel

    >>> # Initializing a M-CTC-T mctct-large style configuration
    >>> configuration = MCTCTConfig()

    >>> # Initializing a model (with random weights) from the mctct-large style configuration
    >>> model = MCTCTModel(configuration)

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