# coding=utf-8 # Copyright 2024 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Wav2Vec2Bert model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class Wav2Vec2BertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Wav2Vec2BertModel`]. It is used to instantiate an Wav2Vec2Bert 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 Wav2Vec2Bert [facebook/wav2vec2-bert-rel-pos-large](https://huggingface.co/facebook/wav2vec2-bert-rel-pos-large) 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*): Vocabulary size of the Wav2Vec2Bert model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Wav2Vec2BertModel`]. Vocabulary size of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`Wav2Vec2BertModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. 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`]. hidden_act (`str` or `function`, *optional*, defaults to `"swish"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. hidden_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. 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`]. 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-05): The epsilon used by the layer normalization layers. 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 procecure generates `mask_time_prob*len(time_axis)/mask_time_length ``independent masks over the axis. If reasoning from the propability 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 procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over the axis. If reasoning from the propability 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 [`Wav2Vec2BertForCTC`]. 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. pad_token_id (`int`, *optional*, defaults to 0): The id of the _beginning-of-stream_ token. bos_token_id (`int`, *optional*, defaults to 1): The id of the _padding_ token. eos_token_id (`int`, *optional*, defaults to 2): The id of the _end-of-stream_ token. 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://arxiv.org/abs/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" def __init__( self, vocab_size=None, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=4096, feature_projection_input_dim=160, hidden_act="swish", hidden_dropout=0.0, activation_dropout=0.0, attention_dropout=0.0, feat_proj_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-5, 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, ctc_loss_reduction="sum", ctc_zero_infinity=False, use_weighted_layer_sum=False, classifier_proj_size=768, tdnn_dim=(512, 512, 512, 512, 1500), tdnn_kernel=(5, 3, 3, 1, 1), tdnn_dilation=(1, 2, 3, 1, 1), xvector_output_dim=512, pad_token_id=0, bos_token_id=1, eos_token_id=2, add_adapter=False, adapter_kernel_size=3, adapter_stride=2, num_adapter_layers=1, adapter_act="relu", use_intermediate_ffn_before_adapter=False, output_hidden_size=None, position_embeddings_type="relative_key", rotary_embedding_base=10000, max_source_positions=5000, left_max_position_embeddings=64, right_max_position_embeddings=8, conv_depthwise_kernel_size=31, conformer_conv_dropout=0.1, **kwargs, ): super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.num_attention_heads = num_attention_heads self.feature_projection_input_dim = feature_projection_input_dim self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.feat_proj_dropout = feat_proj_dropout self.final_dropout = final_dropout self.layerdrop = layerdrop self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.vocab_size = vocab_size self.use_weighted_layer_sum = use_weighted_layer_sum self.max_source_positions = max_source_positions if position_embeddings_type is not None and position_embeddings_type not in [ "rotary", "relative", "relative_key", ]: raise ValueError( """ `position_embeddings_type` is not valid. It must be one of the following values: `["rotary", "relative", "relative_key"]` or left as `None`. """ ) self.position_embeddings_type = position_embeddings_type self.rotary_embedding_base = rotary_embedding_base self.left_max_position_embeddings = left_max_position_embeddings self.right_max_position_embeddings = right_max_position_embeddings # Conformer-block related self.conv_depthwise_kernel_size = conv_depthwise_kernel_size self.conformer_conv_dropout = conformer_conv_dropout # 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 # ctc loss self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity # adapter self.add_adapter = add_adapter self.adapter_kernel_size = adapter_kernel_size self.adapter_stride = adapter_stride self.num_adapter_layers = num_adapter_layers self.adapter_act = adapter_act self.output_hidden_size = output_hidden_size if output_hidden_size is not None else hidden_size if use_intermediate_ffn_before_adapter and not add_adapter: raise ValueError("`use_intermediate_ffn_before_adapter` is `True` but `add_adapter` is `False`.") self.use_intermediate_ffn_before_adapter = use_intermediate_ffn_before_adapter # SequenceClassification-specific parameter. Feel free to ignore for other classes. self.classifier_proj_size = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. self.tdnn_dim = list(tdnn_dim) self.tdnn_kernel = list(tdnn_kernel) self.tdnn_dilation = list(tdnn_dilation) self.xvector_output_dim = xvector_output_dim @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"]