src/transformers/models/hubert/configuration_hubert.py/HubertConfig/__init__
class HubertConfig:    def __init__(
        self,
        vocab_size=32,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout=0.1,
        activation_dropout=0.1,
        attention_dropout=0.1,
        feat_proj_dropout=0.1,
        final_dropout=0.1,
        layerdrop=0.1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        feat_extract_norm="group",
        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,
        do_stable_layer_norm=False,
        apply_spec_augment=True,
        mask_time_prob=0.05,
        mask_time_length=10,
        mask_feature_prob=0.0,
        mask_feature_length=10,
        ctc_loss_reduction="sum",
        ctc_zero_infinity=False,
        use_weighted_layer_sum=False,
        classifier_proj_size=256,
        gradient_checkpointing=False,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        **kwargs
    ):
        super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
        self.hidden_size = hidden_size
        self.feat_extract_norm = feat_extract_norm
        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)
        self.num_hidden_layers = num_hidden_layers
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.feat_proj_dropout = feat_proj_dropout
        self.final_dropout = final_dropout
        self.layerdrop = layerdrop
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm
        self.gradient_checkpointing = gradient_checkpointing
        self.use_weighted_layer_sum = use_weighted_layer_sum
        self.classifier_proj_size = classifier_proj_size

        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)`,"
                f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"
                f"= {len(self.conv_stride)}`, `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_feature_prob = mask_feature_prob
        self.mask_feature_length = mask_feature_length

        # ctc loss
        self.ctc_loss_reduction = ctc_loss_reduction
        self.ctc_zero_infinity = ctc_zero_infinity