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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