262 lines
14 KiB
Python
262 lines
14 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2021 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.
|
||
|
|
""" Hubert model configuration"""
|
||
|
|
|
||
|
|
import functools
|
||
|
|
import operator
|
||
|
|
|
||
|
|
from ...configuration_utils import PretrainedConfig
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
from ..deprecated._archive_maps import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
||
|
|
|
||
|
|
|
||
|
|
class HubertConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
|
||
|
|
Hubert 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 Hubert
|
||
|
|
[facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) 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 Hubert model. Defines the number of different tokens that can be represented by the
|
||
|
|
`inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
|
||
|
|
tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
|
||
|
|
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 [`Wav2Vec2ForCTC`].
|
||
|
|
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_extract_norm (`str`, *optional*, defaults to `"group"`):
|
||
|
|
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
|
||
|
|
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
|
||
|
|
convolutional layers.
|
||
|
|
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
|
||
|
|
The dropout probability for output of the feature encoder.
|
||
|
|
feat_proj_layer_norm (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to apply LayerNorm to the 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]`, *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]`, *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]`, *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.
|
||
|
|
do_stable_layer_norm (`bool`, *optional*, defaults to `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 (`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 [`HubertForCTC`].
|
||
|
|
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 [`HubertForCTC`].
|
||
|
|
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 [`HubertForSequenceClassification`].
|
||
|
|
classifier_proj_size (`int`, *optional*, defaults to 256):
|
||
|
|
Dimensionality of the projection before token mean-pooling for classification.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import HubertModel, HubertConfig
|
||
|
|
|
||
|
|
>>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
|
||
|
|
>>> configuration = HubertConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
|
||
|
|
>>> model = HubertModel(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "hubert"
|
||
|
|
|
||
|
|
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_layer_norm=True,
|
||
|
|
feat_proj_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_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,
|
||
|
|
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_layer_norm = feat_proj_layer_norm
|
||
|
|
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.use_weighted_layer_sum = use_weighted_layer_sum
|
||
|
|
self.classifier_proj_size = classifier_proj_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)`, 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.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
|
||
|
|
|
||
|
|
@property
|
||
|
|
def inputs_to_logits_ratio(self):
|
||
|
|
return functools.reduce(operator.mul, self.conv_stride, 1)
|