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""" SEW-D model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
[docs]class SEWDConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.SEWDModel`. It is used to
instantiate a SEW-D 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 SEW-D `asapp/sew-d-tiny-100k
<https://huggingface.co/asapp/sew-d-tiny-100k>`__ 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 SEW-D model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~transformers.SEWD`.
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.
squeeze_factor (:obj:`int`, `optional`, defaults to 2):
Sequence length downsampling factor after the encoder and upsampling factor after the transformer.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
position_buckets (:obj:`int`, `optional`, defaults to 256):
The maximum size of relative position embeddings.
share_att_key (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to share attention key with c2p and p2c.
relative_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to use relative position encoding.
position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to add absolute position embedding to content embedding.
pos_att_type (:obj:`Tuple[str]`, `optional`, defaults to :obj:`("p2c", "c2p")`):
The type of relative position attention, it can be a combination of :obj:`("p2c", "c2p", "p2p")`, e.g.
:obj:`("p2c")`, :obj:`("p2c", "c2p")`, :obj:`("p2c", "c2p", 'p2p")`.
norm_rel_ebd (:obj:`str`, `optional`, defaults to :obj:`"layer_norm"`):
Whether to use layer norm in relative embedding (:obj:`"layer_norm"` if yes)
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_python"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"`, :obj:`"gelu_python"` and :obj:`"gelu_new"` are supported.
hidden_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (:obj:`float`, `optional`, defaults to 0.1):
The dropout probability for the final projection layer of :class:`SEWDForCTC`.
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-7):
The epsilon used by the layer normalization layers in the transformer encoder.
feature_layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-5):
The epsilon used by the layer normalization after the feature extractor.
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_proj_dropout (:obj:`float`, `optional`, defaults to 0.0):
The dropout probability for output of the 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.
conv_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(64, 128, 128, 128, 128, 256, 256, 256, 256, 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, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`):
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, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`):
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.
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.
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.SEWDForCTC`.
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.SEWDForCTC`.
use_weighted_layer_sum (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of :class:`~transformers.Wav2Vec2ForSequenceClassification`.
classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
Example::
>>> from transformers import SEWDModel, SEWDConfig
>>> # Initializing a SEW-D asapp/sew-d-tiny-100k style configuration
>>> configuration = SEWDConfig()
>>> # Initializing a model from the asapp/sew-d-tiny-100k style configuration
>>> model = SEWDModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "sew-d"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
squeeze_factor=2,
max_position_embeddings=512,
position_buckets=256,
share_att_key=True,
relative_attention=True,
position_biased_input=False,
pos_att_type=("p2c", "c2p"),
norm_rel_ebd="layer_norm",
hidden_act="gelu_python",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-7,
feature_layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),
conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),
conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_feature_prob=0.0,
mask_feature_length=10,
ctc_loss_reduction="mean",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
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.squeeze_factor = squeeze_factor
self.max_position_embeddings = max_position_embeddings
self.position_buckets = position_buckets
self.share_att_key = share_att_key
self.relative_attention = relative_attention
self.norm_rel_ebd = norm_rel_ebd
self.position_biased_input = position_biased_input
self.pos_att_type = list(pos_att_type)
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.feature_layer_norm_eps = feature_layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
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
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
# sequence classification
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size