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# coding=utf-8 | |
# Copyright 2022 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. | |
""" Data2VecText configuration""" | |
import math | |
from ...configuration_utils import PretrainedConfig | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json", | |
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio | |
} | |
class Data2VecAudioConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate | |
an Data2VecAudio 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 Data2VecAudio | |
[facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) 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*, defaults to 32): | |
Vocabulary size of the Data2VecAudio model. Defines the number of different tokens that can be represented | |
by the `inputs_ids` passed when calling [`Data2VecAudioModel`] or [`TFData2VecAudioModel`]. Vocabulary size | |
of the model. Defines the different tokens that can be represented by the *inputs_ids* passed to the | |
forward method of [`Data2VecAudioModel`]. | |
hidden_size (`int`, *optional*, defaults to 768): | |
Dimensionality of the encoder layers and the pooler layer. | |
num_hidden_layers (`int`, *optional*, defaults to 12): | |
Number of hidden layers in the Transformer encoder. | |
num_attention_heads (`int`, *optional*, defaults to 12): | |
Number of attention heads for each attention layer in the Transformer encoder. | |
intermediate_size (`int`, *optional*, defaults to 3072): | |
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
`"relu"`, `"selu"` and `"gelu_new"` are supported. | |
hidden_dropout (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
activation_dropout (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for activations inside the fully connected layer. | |
attention_dropout (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention probabilities. | |
final_dropout (`float`, *optional*, defaults to 0.1): | |
The dropout probability for the final projection layer of [`Data2VecAudioForCTC`]. | |
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-12): | |
The epsilon used by the layer normalization layers. | |
feat_proj_dropout (`float`, *optional*, defaults to 0.0): | |
The dropout probability for output of the feature encoder. | |
feat_extract_activation (`str, `optional`, defaults to `"gelu"`): | |
The non-linear activation function (function or string) in the 1D convolutional layers of the feature | |
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. | |
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(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 encoder. The length of *conv_dim* defines the number of 1D convolutional layers. | |
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): | |
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length | |
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. | |
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`): | |
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The | |
length of *conv_kernel* defines the number of convolutional layers and has to match the length of | |
*conv_dim*. | |
conv_bias (`bool`, *optional*, defaults to `False`): | |
Whether the 1D convolutional layers have a bias. | |
num_conv_pos_embeddings (`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 (`int`, *optional*, defaults to 16): | |
Number of groups of 1D convolutional positional embeddings layer. | |
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 | |
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 [`Data2VecAudioForCTC`]. | |
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 [`Data2VecAudioForCTC`]. | |
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 [`Data2VecAudioForSequenceClassification`]. | |
classifier_proj_size (`int`, *optional*, defaults to 256): | |
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 network should be stacked on top of the Data2VecAudio Encoder. Can be very useful | |
for warm-starting Data2VecAudio 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 3): | |
Number of convolutional layers that should be used in 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`. | |
Example: | |
```python | |
>>> from transformers import Data2VecAudioConfig, Data2VecAudioModel | |
>>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration | |
>>> configuration = Data2VecAudioConfig() | |
>>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration | |
>>> model = Data2VecAudioModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "data2vec-audio" | |
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=0.0, | |
final_dropout=0.1, | |
layerdrop=0.1, | |
initializer_range=0.02, | |
layer_norm_eps=1e-5, | |
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_embedding_groups=16, | |
conv_pos_kernel_size=19, | |
num_conv_pos_embeddings=5, | |
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=256, | |
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=3, | |
output_hidden_size=None, | |
**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_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.conv_pos_kernel_size = conv_pos_kernel_size | |
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.use_weighted_layer_sum = use_weighted_layer_sum | |
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)`, but is `len(config.conv_dim) =" | |
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`," | |
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`." | |
) | |
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 | |
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.output_hidden_size = output_hidden_size or hidden_size | |
# 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 | |
def inputs_to_logits_ratio(self): | |
return math.prod(self.conv_stride) | |