257 lines
14 KiB
Python
257 lines
14 KiB
Python
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# coding=utf-8
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# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" SEW model configuration"""
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import functools
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import operator
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class SEWConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SEWModel`]. It is used to instantiate a SEW model
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according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the SEW
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[asapp/sew-tiny-100k](https://huggingface.co/asapp/sew-tiny-100k) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32):
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Vocabulary size of the SEW model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`SEW`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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squeeze_factor (`int`, *optional*, defaults to 2):
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Sequence length downsampling factor after the encoder and upsampling factor after the transformer.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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activation_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for activations inside the fully connected layer.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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final_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for the final projection layer of [`SEWForCTC`].
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layerdrop (`float`, *optional*, defaults to 0.1):
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The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
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details.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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feat_extract_norm (`str`, *optional*, defaults to `"group"`):
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The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
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normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
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convolutional layers.
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feat_proj_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for output of the feature encoder.
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feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the 1D convolutional layers of the feature
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extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
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conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`):
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A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
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feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
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conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`):
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A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
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of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
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conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`):
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A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
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length of *conv_kernel* defines the number of convolutional layers and has to match the length of
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*conv_dim*.
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conv_bias (`bool`, *optional*, defaults to `False`):
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Whether the 1D convolutional layers have a bias.
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num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
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Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
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embeddings layer.
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num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
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Number of groups of 1D convolutional positional embeddings layer.
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apply_spec_augment (`bool`, *optional*, defaults to `True`):
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Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
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[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
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Recognition](https://arxiv.org/abs/1904.08779).
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mask_time_prob (`float`, *optional*, defaults to 0.05):
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Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
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procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
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reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
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masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
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actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
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mask_time_length (`int`, *optional*, defaults to 10):
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Length of vector span along the time axis.
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mask_time_min_masks (`int`, *optional*, defaults to 2),:
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The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
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irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
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mask_time_min_masks''
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mask_feature_prob (`float`, *optional*, defaults to 0.0):
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Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
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masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
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the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
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span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
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may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
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True`.
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mask_feature_length (`int`, *optional*, defaults to 10):
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Length of vector span along the feature axis.
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mask_feature_min_masks (`int`, *optional*, defaults to 0),:
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The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
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step, irrespectively of `mask_feature_prob`. Only relevant if
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''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
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ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
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Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
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instance of [`SEWForCTC`].
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ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
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Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
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occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
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of [`SEWForCTC`].
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use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
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Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
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instance of [`Wav2Vec2ForSequenceClassification`].
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classifier_proj_size (`int`, *optional*, defaults to 256):
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Dimensionality of the projection before token mean-pooling for classification.
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Example:
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```python
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>>> from transformers import SEWConfig, SEWModel
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>>> # Initializing a SEW asapp/sew-tiny-100k style configuration
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>>> configuration = SEWConfig()
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>>> # Initializing a model (with random weights) from the asapp/sew-tiny-100k style configuration
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>>> model = SEWModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "sew"
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def __init__(
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self,
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vocab_size=32,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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squeeze_factor=2,
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hidden_act="gelu",
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hidden_dropout=0.1,
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activation_dropout=0.1,
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attention_dropout=0.1,
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feat_proj_dropout=0.0,
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final_dropout=0.1,
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layerdrop=0.1,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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feat_extract_norm="group",
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feat_extract_activation="gelu",
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conv_dim=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),
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conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),
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conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),
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conv_bias=False,
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num_conv_pos_embeddings=128,
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num_conv_pos_embedding_groups=16,
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apply_spec_augment=True,
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mask_time_prob=0.05,
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mask_time_length=10,
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mask_time_min_masks=2,
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mask_feature_prob=0.0,
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mask_feature_length=10,
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mask_feature_min_masks=0,
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ctc_loss_reduction="mean",
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ctc_zero_infinity=False,
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use_weighted_layer_sum=False,
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classifier_proj_size=256,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
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self.hidden_size = hidden_size
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self.feat_extract_norm = feat_extract_norm
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self.feat_extract_activation = feat_extract_activation
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self.conv_dim = list(conv_dim)
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self.conv_stride = list(conv_stride)
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self.conv_kernel = list(conv_kernel)
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self.conv_bias = conv_bias
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self.num_conv_pos_embeddings = num_conv_pos_embeddings
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self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
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self.num_feat_extract_layers = len(self.conv_dim)
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.squeeze_factor = squeeze_factor
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self.hidden_act = hidden_act
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self.num_attention_heads = num_attention_heads
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.feat_proj_dropout = feat_proj_dropout
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self.final_dropout = final_dropout
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self.layerdrop = layerdrop
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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self.vocab_size = vocab_size
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if (
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(len(self.conv_stride) != self.num_feat_extract_layers)
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or (len(self.conv_kernel) != self.num_feat_extract_layers)
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or (len(self.conv_dim) != self.num_feat_extract_layers)
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):
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raise ValueError(
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"Configuration for convolutional layers is incorrect. "
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"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, "
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f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride) "
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f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`."
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)
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# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
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self.apply_spec_augment = apply_spec_augment
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self.mask_time_prob = mask_time_prob
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self.mask_time_length = mask_time_length
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self.mask_time_min_masks = mask_time_min_masks
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self.mask_feature_prob = mask_feature_prob
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self.mask_feature_length = mask_feature_length
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self.mask_feature_min_masks = mask_feature_min_masks
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# ctc loss
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self.ctc_loss_reduction = ctc_loss_reduction
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self.ctc_zero_infinity = ctc_zero_infinity
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# sequence classification
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self.use_weighted_layer_sum = use_weighted_layer_sum
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self.classifier_proj_size = classifier_proj_size
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@property
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def inputs_to_logits_ratio(self):
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return functools.reduce(operator.mul, self.conv_stride, 1)
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