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"""Wav2Vec2Bert model configuration""" from typing import Literal from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring @auto_docstring(checkpoint="facebook/wav2vec2-bert-rel-pos-large") @strict class Wav2Vec2BertConfig(PreTrainedConfig): r""" feature_projection_input_dim (`int`, *optional*, defaults to 160): Input dimension of this model, i.e the dimension after processing input audios with [`SeamlessM4TFeatureExtractor`] or [`Wav2Vec2BertProcessor`]. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the feature projection. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Wav2Vec2BertForCTC`]. 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_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 [`Wav2Vec2BertForCTC`]. 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 [`Wav2Vec2BertForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 768): Dimensionality of the projection before token mean-pooling for classification. tdnn_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`): A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN* module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers. tdnn_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*. tdnn_dilation (`tuple[int]` or `list[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`): A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*. xvector_output_dim (`int`, *optional*, defaults to 512): Dimensionality of the *XVector* embedding vectors. add_adapter (`bool`, *optional*, defaults to `False`): Whether a convolutional attention network should be stacked on top of the Wav2Vec2Bert Encoder. Can be very useful for warm-starting Wav2Vec2Bert for SpeechEncoderDecoder models. adapter_kernel_size (`int`, *optional*, defaults to 3): Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. adapter_stride (`int`, *optional*, defaults to 2): Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. num_adapter_layers (`int`, *optional*, defaults to 1): Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is True`. adapter_act (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the adapter layers. If string, `"gelu"`, `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. use_intermediate_ffn_before_adapter (`bool`, *optional*, defaults to `False`): Whether an intermediate feed-forward block should be stacked on top of the Wav2Vec2Bert Encoder and before the adapter network. Only relevant if `add_adapter is True`. output_hidden_size (`int`, *optional*): Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant if `add_adapter is True`. position_embeddings_type (`str`, *optional*, defaults to `"relative_key"`): Can be specified to : - `rotary`, for rotary position embeddings. - `relative`, for relative position embeddings. - `relative_key`, for relative position embeddings as defined by Shaw in [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155). If left to `None`, no relative position embeddings is applied. rotary_embedding_base (`int`, *optional*, defaults to 10000): If `"rotary"` position embeddings are used, defines the size of the embedding base. max_source_positions (`int`, *optional*, defaults to 5000): if `"relative"` position embeddings are used, defines the maximum source input positions. left_max_position_embeddings (`int`, *optional*, defaults to 64): If `"relative_key"` (aka Shaw) position embeddings are used, defines the left clipping value for relative positions. right_max_position_embeddings (`int`, *optional*, defaults to 8): If `"relative_key"` (aka Shaw) position embeddings are used, defines the right clipping value for relative positions. conv_depthwise_kernel_size (`int`, *optional*, defaults to 31): Kernel size of convolutional depthwise 1D layer in Conformer blocks. conformer_conv_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all convolutional layers in Conformer blocks. Example: ```python >>> from transformers import Wav2Vec2BertConfig, Wav2Vec2BertModel >>> # Initializing a Wav2Vec2Bert facebook/wav2vec2-bert-rel-pos-large style configuration >>> configuration = Wav2Vec2BertConfig() >>> # Initializing a model (with random weights) from the facebook/wav2vec2-bert-rel-pos-large style configuration >>> model = Wav2Vec2BertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "wav2vec2-bert" vocab_size: int | None = None hidden_size: int = 1024 num_hidden_layers: int = 24 num_attention_heads: int = 16 intermediate_size: int = 4096 feature_projection_input_dim: int = 160 hidden_act: str = "swish" hidden_dropout: float | int = 0.0 activation_dropout: float | int = 0.0 attention_dropout: float | int = 0.0 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 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 = 768 tdnn_dim: list[int] | tuple[int, ...] = (512, 512, 512, 512, 1500) tdnn_kernel: list[int] | tuple[int, ...] = (5, 3, 3, 1, 1) tdnn_dilation: list[int] | tuple[int, ...] = (1, 2, 3, 1, 1) xvector_output_dim: int = 512 pad_token_id: int | None = 0 bos_token_id: int | None = 1 eos_token_id: int | list[int] | None = 2 add_adapter: bool = False adapter_kernel_size: int = 3 adapter_stride: int = 2 num_adapter_layers: int = 1 adapter_act: str = "relu" use_intermediate_ffn_before_adapter: bool = False output_hidden_size: int | None = None position_embeddings_type: Literal["rotary", "relative", "relative_key"] | None = "relative_key" rotary_embedding_base: int = 10000 max_source_positions: int = 5000 left_max_position_embeddings: int = 64 right_max_position_embeddings: int = 8 conv_depthwise_kernel_size: int = 31 conformer_conv_dropout: float | int = 0.1 def __post_init__(self, **kwargs): self.output_hidden_size = self.output_hidden_size or self.hidden_size super().__post_init__(**kwargs) def validate_architecture(self): """Part of `@strict`-powered validation. Validates the architecture of the config.""" if self.use_intermediate_ffn_before_adapter and not self.add_adapter: raise ValueError("`use_intermediate_ffn_before_adapter` is `True` but `add_adapter` is `False`.") @property def inputs_to_logits_ratio(self): ratio = self.feature_projection_input_dim * 2 if self.add_adapter: ratio = ratio * (self.adapter_stride**self.num_adapter_layers) return ratio __all__ = ["Wav2Vec2BertConfig"]