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"""Hubert model configuration""" import functools import operator from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring @auto_docstring(checkpoint="facebook/hubert-base-ls960") @strict class HubertConfig(PreTrainedConfig): r""" feat_proj_layer_norm (`bool`, *optional*, defaults to `True`): Whether to apply LayerNorm to the output of the feature encoder. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`]. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. 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]`, *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]`, *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]`, *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. conv_pos_batch_norm (`bool`, *optional*, defaults to `False`): Whether to use batch norm instead of weight norm in conv_pos do_stable_layer_norm (`bool`, *optional*, defaults to `False`): Whether do 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://huggingface.co/papers/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 procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability 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 procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability 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'' 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 [`HubertForCTC`]. 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 [`HubertForCTC`]. 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 [`HubertForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. Example: ```python >>> from transformers import HubertModel, HubertConfig >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration >>> configuration = HubertConfig() >>> # Initializing a model from the facebook/hubert-base-ls960 style configuration >>> model = HubertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "hubert" vocab_size: int = 32 hidden_size: int = 768 num_hidden_layers: int = 12 num_attention_heads: int = 12 intermediate_size: int = 3072 hidden_act: str = "gelu" hidden_dropout: float | int = 0.1 activation_dropout: float | int = 0.1 attention_dropout: float | int = 0.1 feat_proj_layer_norm: bool = True feat_proj_dropout: float | int = 0.0 final_dropout: float | int = 0.1 layerdrop: float | int = 0.1 initializer_range: float = 0.02 layer_norm_eps: float = 1e-5 feat_extract_norm: str = "group" feat_extract_activation: str = "gelu" conv_dim: list[int] | tuple[int, ...] = (512, 512, 512, 512, 512, 512, 512) conv_stride: list[int] | tuple[int, ...] = (5, 2, 2, 2, 2, 2, 2) conv_kernel: list[int] | tuple[int, ...] = (10, 3, 3, 3, 3, 2, 2) conv_bias: bool = False num_conv_pos_embeddings: int = 128 num_conv_pos_embedding_groups: int = 16 conv_pos_batch_norm: bool = False do_stable_layer_norm: bool = False apply_spec_augment: bool = True mask_time_prob: float | int = 0.05 mask_time_length: int = 10 mask_time_min_masks: int = 2 mask_feature_prob: float | int = 0.0 mask_feature_length: int = 10 mask_feature_min_masks: int = 0 ctc_loss_reduction: str = "sum" ctc_zero_infinity: bool = False use_weighted_layer_sum: bool = False classifier_proj_size: int = 256 pad_token_id: int | None = 0 bos_token_id: int | None = 1 eos_token_id: int | list[int] | None = 2 def __post_init__(self, **kwargs): self.num_feat_extract_layers = len(self.conv_dim) super().__post_init__(**kwargs) def validate_architecture(self): """Part of `@strict`-powered validation. Validates the architecture of the config.""" 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)}`." ) @property def inputs_to_logits_ratio(self): return functools.reduce(operator.mul, self.conv_stride, 1) __all__ = ["HubertConfig"]