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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
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+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
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+[More Information Needed]
+
+### Compute Infrastructure
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+#### Hardware
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+## Model Card Contact
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config.json

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+{
+  "activation_dropout": 0.0,
+  "adapter_attn_dim": null,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForPreTraining"
+  ],
+  "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_wav2vec2_spkreg.Wav2Vec2SpkRegConfig"
+  },
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": false,
+  "easy_margin": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "freeze_feat_extract_train": true,
+  "gradient_checkpointing": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label_smoothing": 0.0,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "loss_fct": "cross_entropy",
+  "margin": 0.35,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2_spkreg",
+  "no_mask_channel_overlap": false,
+  "no_mask_time_overlap": false,
+  "num_adapter_layers": 3,
+  "num_attention_heads": 12,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "reduction": "mean",
+  "scale": 30.0,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "transformers_version": "4.46.2",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "xvector_output_dim": 512
+}

configuration_wav2vec2_spkreg.py

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+"""Wav2Vec2 model configuration"""
+
+import functools
+import operator
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Wav2Vec2SpkRegConfig(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.
+        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_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 probability for quantized feature encoder states.
+        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.
+        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 probability 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]` 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 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`.
+        adapter_attn_dim (`int`, *optional*):
+            Dimension of the attention adapter weights to be used in each attention block. An example of a model using
+            attention adapters is [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all).
+        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 Wav2Vec2Config, Wav2Vec2Model
+
+    >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration
+    >>> configuration = Wav2Vec2Config()
+
+    >>> # Initializing a model (with random weights) from the facebook/wav2vec2-base-960h style configuration
+    >>> model = Wav2Vec2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "wav2vec2_spkreg"
+
+    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,
+        adapter_attn_dim=None,
+        loss_fct: str = 'cross_entropy', # cross_entropy, additive_margin, additive_angular_margin
+        label_smoothing: float = 0.0, 
+        scale: float = 30.0, 
+        margin: float = 0.35, 
+        easy_margin: bool = False, 
+        reduction: str = "mean", 
+        **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
+
+        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
+
+        # 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
+        self.adapter_attn_dim = adapter_attn_dim
+
+        # 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
+
+        # Loss function parameters. Feel free to ignore for other classes.
+        self.loss_fct = loss_fct
+        self.label_smoothing = label_smoothing
+        self.scale = scale
+        self.margin = margin
+        self.easy_margin = easy_margin
+        self.reduction = reduction
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return functools.reduce(operator.mul, self.conv_stride, 1)