src/transformers/models/speecht5/configuration_speecht5.py/SpeechT5Config/__init__
class SpeechT5Config:    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_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_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,
        )