Source code for transformers.models.wav2vec2.configuration_wav2vec2

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""" Wav2Vec2 model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


[docs]class Wav2Vec2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.Wav2Vec2Model`. It is used to instantiate an Wav2Vec2 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 Wav2Vec2 `facebook/wav2vec2-base-960h <https://huggingface.co/facebook/wav2vec2-base-960h>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 32): Vocabulary size of the Wav2Vec2 model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.Wav2Vec2Model` or :class:`~transformers.TFWav2Vec2Model`. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.Wav2Vec2Model`. hidden_size (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): The epsilon used by the layer normalization layers. feat_extract_norm (:obj:`str`, `optional`, defaults to :obj:`"group"`): The norm to be applied to 1D convolutional layers in feature extractor. One of :obj:`"group"` for group normalization of only the first 1D convolutional layer or :obj:`"layer"` for layer normalization of all 1D convolutional layers. feat_extract_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all 1D convolutional layers in feature extractor. feat_extract_activation (:obj:`str, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. feat_quantizer_dropout (obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for quantized feature extractor states. conv_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(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 extractor. The length of `conv_dim` defines the number of 1D convolutional layers. conv_stride (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 2, 2, 2, 2, 2, 2)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature extractor. The length of `conv_stride` defines the number of convolutional layers and has to match the the length of `conv_dim`. conv_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(10, 3, 3, 3, 3, 3, 3)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature extractor. The length of `conv_kernel` defines the number of convolutional layers and has to match the the length of `conv_dim`. conv_bias (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. do_stable_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`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 (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature extractor. For reference see `SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition <https://arxiv.org/abs/1904.08779>`__. mask_time_prob (:obj:`float`, `optional`, defaults to 0.05): Propability of each feature vector along the time axis to be chosen as the start of the vector span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``. mask_time_length (:obj:`int`, `optional`, defaults to 10): Length of vector span along the time axis. mask_feature_prob (:obj:`float`, `optional`, defaults to 0.0): Propability of each feature vector along the feature axis to be chosen as the start of the vector span to be masked. Approximately ``mask_time_prob * hidden_size // mask_time_length`` feature vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``. mask_feature_length (:obj:`int`, `optional`, defaults to 10): Length of vector span along the feature axis. num_codevectors_per_group (:obj:`int`, `optional`, defaults to 320): Number of entries in each quantization codebook (group). num_codevector_groups (:obj:`int`, `optional`, defaults to 2): Number of codevector groups for product codevector quantization. contrastive_logits_temperature (:obj:`float`, `optional`, defaults to 0.1): The temperature `kappa` in the contrastive loss. feat_quantizer_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for the output of the feature extractor that's used by the quantizer. num_negatives (:obj:`int`, `optional`, defaults to 100): Number of negative samples for the contrastive loss. codevector_dim (:obj:`int`, `optional`, defaults to 256): Dimensionality of the quantized feature vectors. proj_codevector_dim (:obj:`int`, `optional`, defaults to 256): Dimensionality of the final projection of both the quantized and the transformer features. diversity_loss_weight (:obj:`int`, `optional`, defaults to 0.1): The weight of the codebook diversity loss component. ctc_loss_reduction (:obj:`str`, `optional`, defaults to :obj:`"sum"`): Specifies the reduction to apply to the output of ``torch.nn.CTCLoss``. Only relevant when training an instance of :class:`~transformers.Wav2Vec2ForCTC`. ctc_zero_infinity (:obj:`bool`, `optional`, defaults to :obj:`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 :class:`~transformers.Wav2Vec2ForCTC`. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. Example:: >>> from transformers import Wav2Vec2Model, Wav2Vec2Config >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration >>> configuration = Wav2Vec2Config() >>> # Initializing a model from the facebook/wav2vec2-base-960h style configuration >>> model = Wav2Vec2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "wav2vec2" def __init__( self, vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_dropout=0.1, feat_quantizer_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-5, feat_extract_norm="group", feat_extract_activation="gelu", conv_dim=(512, 512, 512, 512, 512, 512, 512), conv_stride=(5, 2, 2, 2, 2, 2, 2), conv_kernel=(10, 3, 3, 3, 3, 2, 2), conv_bias=False, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16, do_stable_layer_norm=False, apply_spec_augment=True, mask_time_prob=0.05, mask_time_length=10, mask_feature_prob=0.0, mask_feature_length=10, num_codevectors_per_group=320, num_codevector_groups=2, contrastive_logits_temperature=0.1, num_negatives=100, codevector_dim=256, proj_codevector_dim=256, diversity_loss_weight=0.1, ctc_loss_reduction="sum", ctc_zero_infinity=False, gradient_checkpointing=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, **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.feat_extract_norm = feat_extract_norm self.feat_extract_activation = feat_extract_activation self.conv_dim = list(conv_dim) self.conv_stride = list(conv_stride) self.conv_kernel = list(conv_kernel) self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_feat_extract_layers = len(self.conv_dim) 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.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.do_stable_layer_norm = do_stable_layer_norm self.gradient_checkpointing = gradient_checkpointing 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)`," f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)" f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`." ) # 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_feature_prob = mask_feature_prob self.mask_feature_length = mask_feature_length # parameters for pretraining with codevector quantized representations self.num_codevectors_per_group = num_codevectors_per_group self.num_codevector_groups = num_codevector_groups self.contrastive_logits_temperature = contrastive_logits_temperature self.feat_quantizer_dropout = feat_quantizer_dropout self.num_negatives = num_negatives self.codevector_dim = codevector_dim self.proj_codevector_dim = proj_codevector_dim self.diversity_loss_weight = diversity_loss_weight # ctc loss self.ctc_loss_reduction = ctc_loss_reduction self.ctc_zero_infinity = ctc_zero_infinity