i0(zdZddlZddlZddlmZddlmZddlmZedeGd d eZ d gZ y) zHubert model configurationN)strict)PreTrainedConfig)auto_docstringzfacebook/hubert-base-ls960) checkpointceZdZUdZdZdZeed<dZeed<dZ eed<dZ eed <d Z eed <d Z e ed <dZeezed<dZeezed<dZeezed<dZeed<dZeezed<dZeezed<dZeezed<dZeed<dZeed<dZe ed<d Ze ed<dZeeeed fzed!<d"Zeeeed fzed#<d$Zeeeed fzed%<d&Z eed'<d(Z!eed)<d*Z"eed+<d&Z#eed,<d&Z$eed-<dZ%eed.<d/Z&eezed0<d1Z'eed2<d3Z(eed4<dZ)eezed5<d1Z*eed6<d7Z+eed8<d9Z,e ed:<d&Z-eed;<d&Z.eed<<d=Z/eed><d7Z0ed?zed@<dAZ1ed?zedB<d3Z2eeezd?zedC<fdDZ3dEZ4e5dFZ6xZ7S)G HubertConfiga. 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 ```hubert vocab_sizei hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actg?hidden_dropoutactivation_dropoutattention_dropoutTfeat_proj_layer_normgfeat_proj_dropout final_dropout layerdropg{Gz?initializer_rangegh㈵>layer_norm_epsgroupfeat_extract_normfeat_extract_activation)r r r r r r .conv_dim)r#r#r#r#r# conv_stride) rrrrr#r# conv_kernelF conv_biasnum_conv_pos_embeddingsnum_conv_pos_embedding_groupsconv_pos_batch_normdo_stable_layer_normapply_spec_augmentg?mask_time_probr%mask_time_lengthr#mask_time_min_masksmask_feature_probmask_feature_lengthrmask_feature_min_maskssumctc_loss_reductionctc_zero_infinityuse_weighted_layer_sumclassifier_proj_sizeN pad_token_id bos_token_id eos_token_idc Xt|j|_t|di|y)N)lenr!num_feat_extract_layerssuper __post_init__)selfkwargs __class__s /var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/hubert/configuration_hubert.pyrDzHubertConfig.__post_init__s$'*4=='9$ ''c lt|j|jk7sDt|j|jk7s"t|j|jk7rNt dt|jdt|jdt|jdy)zOPart of `@strict`-powered validation. Validates the architecture of the config.zConfiguration 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) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.N)rAr$rBr&r! ValueErrorrEs rHvalidate_architecturez"HubertConfig.validate_architectures!! 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