wav2vec2-ctc-tedlium / models /configuration_wav2vec2.py

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	# 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.
	""" Wav2Vec2 model configuration"""

	import functools
	import operator

	from transformers.configuration_utils import PretrainedConfig
	from transformers.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
	}


	class Wav2Vec2Config(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`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 [`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 Wav2Vec2 model. Defines the number of different tokens that can be represented by
	the `inputs_ids` passed when calling [`Wav2Vec2Model`] or [`TFWav2Vec2Model`]. Vocabulary size of the
	model. Defines the different tokens that can be represented by the inputs_ids passed to the forward
	method of [`Wav2Vec2Model`].
	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.
	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`].
	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_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.
	feat_quantizer_dropout (`float`, optional, defaults to 0.0):
	The dropout probabilitiy for quantized feature encoder states.
	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 to 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''
	num_codevectors_per_group (`int`, optional, defaults to 320):
	Number of entries in each quantization codebook (group).
	num_codevector_groups (`int`, optional, defaults to 2):
	Number of codevector groups for product codevector quantization.
	contrastive_logits_temperature (`float`, optional, defaults to 0.1):
	The temperature kappa in the contrastive loss.
	feat_quantizer_dropout (`float`, optional, defaults to 0.0):
	The dropout probabilitiy for the output of the feature encoder that's used by the quantizer.
	num_negatives (`int`, optional, defaults to 100):
	Number of negative samples for the contrastive loss.
	codevector_dim (`int`, optional, defaults to 256):
	Dimensionality of the quantized feature vectors.
	proj_codevector_dim (`int`, optional, defaults to 256):
	Dimensionality of the final projection of both the quantized and the transformer features.
	diversity_loss_weight (`int`, optional, defaults to 0.1):
	The weight of the codebook diversity loss component.
	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 [`Wav2Vec2ForCTC`].
	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 [`Wav2Vec2ForCTC`].
	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 [`Wav2Vec2ForSequenceClassification`].
	classifier_proj_size (`int`, optional, defaults to 256):
	Dimensionality of the projection before token mean-pooling for classification.
	tdnn_dim (`Tuple[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]`, 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]`, 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 Wav2Vec2 Encoder. Can be very useful for
	warm-starting Wav2Vec2 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`.
	use_scan (`bool`, optional, defaults to `False`):
	Whether or not to use nn.scan in the Flax Wav2Vec2 transformer layers.

	Example:

	```python
	>>> 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=0.0,
	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_time_min_masks=2,
	mask_feature_prob=0.0,
	mask_feature_length=10,
	mask_feature_min_masks=0,
	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,
	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,
	use_scan=False,
	fuse_matmuls=False,
	**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.use_weighted_layer_sum = use_weighted_layer_sum
	self.use_scan = use_scan
	self.fuse_matmuls = fuse_matmuls

	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_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

	# 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

	# 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

	@property
	def inputs_to_logits_ratio(self):
	return functools.reduce(operator.mul, self.conv_stride, 1)