o ZŽhZ:ã@sPdZddlZddlZddlmZddlmZe e¡Z Gdd„deƒZ dgZ dS)zHubert model configurationéNé)ÚPretrainedConfig)Úloggingcs€eZdZdZdZ                                d‡fdd„ Zedd„ƒZ‡ZS) Ú HubertConfiga6& This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Hubert [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32): Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout(`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. attention_dropout(`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`]. layerdrop (`float`, *optional*, defaults to 0.1): The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. 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_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. feat_proj_layer_norm (`bool`, *optional*, defaults to `True`): Whether to apply LayerNorm to the output of the feature encoder. 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://arxiv.org/abs/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 ```Zhuberté éé é Úgeluçš™™™™™¹?Tçç{®Gáz”?çñh㈵øä>Úgroup©érrrrrr©éérrrrr©é rrrrrrFé€éçš™™™™™©?rrrÚsumééc() sptƒjdi|(¤|%|&|'dœ¤Ž||_||_||_t|ƒ|_t|ƒ|_t|ƒ|_||_ ||_ ||_ ||_ t |jƒ|_||_||_||_||_||_| |_||_| |_| |_| |_| |_||_||_||_||_|#|_|$|_t |jƒ|jks„t |jƒ|jks„t |jƒ|jkr›t dt |jƒ›dt |jƒ›dt |jƒ›dƒ‚||_!||_"||_#||_$||_%||_&| |_'|!|_(|"|_)dS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_idzºConfiguration 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`.©)*ÚsuperÚ__init__Ú hidden_sizeÚfeat_extract_normÚfeat_extract_activationÚlistÚconv_dimÚ conv_strideÚ conv_kernelÚ conv_biasÚnum_conv_pos_embeddingsÚnum_conv_pos_embedding_groupsÚconv_pos_batch_normÚlenZnum_feat_extract_layersÚnum_hidden_layersÚintermediate_sizeÚ hidden_actÚnum_attention_headsÚhidden_dropoutÚattention_dropoutÚactivation_dropoutÚfeat_proj_layer_normÚfeat_proj_dropoutÚ final_dropoutÚ layerdropÚlayer_norm_epsÚinitializer_rangeÚ vocab_sizeÚdo_stable_layer_normÚuse_weighted_layer_sumÚclassifier_proj_sizeÚ ValueErrorÚapply_spec_augmentÚmask_time_probÚmask_time_lengthÚmask_time_min_masksÚmask_feature_probÚmask_feature_lengthÚmask_feature_min_masksÚctc_loss_reductionÚctc_zero_infinity))Úselfr<r#r/r2r0r1r3r5r4r6r7r8r9r;r:r$r%r'r(r)r*r+r,r-r=rArBrCrDrErFrGrHrIr>r?rrrÚkwargs©Ú __class__r ú^/var/www/auris/lib/python3.10/site-packages/transformers/models/hubert/configuration_hubert.pyr"¢sd+    þþýÿ zHubertConfig.__init__cCst tj|jd¡S)Nr)Ú functoolsÚreduceÚoperatorÚmulr()rJr r rNÚinputs_to_logits_ratiosz#HubertConfig.inputs_to_logits_ratio)'rrrrr r r r r Tr r r r rrr rrrFrrFFTrrrr rrrFFrrrr) Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typer"ÚpropertyrSÚ __classcell__r r rLrNrs\Øbr) rWrOrQZconfiguration_utilsrÚutilsrZ get_loggerrTÚloggerrÚ__all__r r r rNÚs    o