---
local: wav2vec2
sections:
- local: overview
  title: Overview
- local: transformers.Wav2Vec2Config
  title: Wav2Vec2Config
- local: transformers.Wav2Vec2CTCTokenizer
  title: Wav2Vec2CTCTokenizer
- local: transformers.Wav2Vec2FeatureExtractor
  title: Wav2Vec2FeatureExtractor
- local: transformers.Wav2Vec2Processor
  title: Wav2Vec2Processor
- local: transformers.Wav2Vec2ProcessorWithLM
  title: Wav2Vec2ProcessorWithLM
- local: transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
  title: Wav2Vec2 specific outputs
- local: transformers.Wav2Vec2Model
  title: Wav2Vec2Model
- local: transformers.Wav2Vec2ForCTC
  title: Wav2Vec2ForCTC
- local: transformers.Wav2Vec2ForSequenceClassification
  title: Wav2Vec2ForSequenceClassification
- local: transformers.Wav2Vec2ForAudioFrameClassification
  title: Wav2Vec2ForAudioFrameClassification
- local: transformers.Wav2Vec2ForXVector
  title: Wav2Vec2ForXVector
- local: transformers.Wav2Vec2ForPreTraining
  title: Wav2Vec2ForPreTraining
- local: transformers.TFWav2Vec2Model
  title: TFWav2Vec2Model
- local: transformers.TFWav2Vec2ForCTC
  title: TFWav2Vec2ForCTC
- local: transformers.FlaxWav2Vec2Model
  title: FlaxWav2Vec2Model
- local: transformers.FlaxWav2Vec2ForCTC
  title: FlaxWav2Vec2ForCTC
- local: transformers.FlaxWav2Vec2ForPreTraining
  title: FlaxWav2Vec2ForPreTraining
title: Wav2Vec2
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
<!--Copyright 2021 The HuggingFace 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.
-->

<h1 id="wav2vec2">Wav2Vec2</h1>

<h2 id="overview">Overview</h2>

The Wav2Vec2 model was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

The abstract from the paper is the following:

*We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on
transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks
the speech input in the latent space and solves a contrastive task defined over a quantization of the latent
representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the
clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state
of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and
pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech
recognition with limited amounts of labeled data.*

Tips:

- Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer).

This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).


<h2 id="transformers.Wav2Vec2Config">Wav2Vec2Config</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2Config</name><anchor>transformers.Wav2Vec2Config</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/configuration_wav2vec2.py#L29</source><parameters>[{"name": "vocab_size", "val": " = 32"}, {"name": "hidden_size", "val": " = 768"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 12"}, {"name": "intermediate_size", "val": " = 3072"}, {"name": "hidden_act", "val": " = 'gelu'"}, {"name": "hidden_dropout", "val": " = 0.1"}, {"name": "activation_dropout", "val": " = 0.1"}, {"name": "attention_dropout", "val": " = 0.1"}, {"name": "feat_proj_dropout", "val": " = 0.0"}, {"name": "feat_quantizer_dropout", "val": " = 0.0"}, {"name": "final_dropout", "val": " = 0.1"}, {"name": "layerdrop", "val": " = 0.1"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "feat_extract_norm", "val": " = 'group'"}, {"name": "feat_extract_activation", "val": " = 'gelu'"}, {"name": "conv_dim", "val": " = (512, 512, 512, 512, 512, 512, 512)"}, {"name": "conv_stride", "val": " = (5, 2, 2, 2, 2, 2, 2)"}, {"name": "conv_kernel", "val": " = (10, 3, 3, 3, 3, 2, 2)"}, {"name": "conv_bias", "val": " = False"}, {"name": "num_conv_pos_embeddings", "val": " = 128"}, {"name": "num_conv_pos_embedding_groups", "val": " = 16"}, {"name": "do_stable_layer_norm", "val": " = False"}, {"name": "apply_spec_augment", "val": " = True"}, {"name": "mask_time_prob", "val": " = 0.05"}, {"name": "mask_time_length", "val": " = 10"}, {"name": "mask_time_min_masks", "val": " = 2"}, {"name": "mask_feature_prob", "val": " = 0.0"}, {"name": "mask_feature_length", "val": " = 10"}, {"name": "mask_feature_min_masks", "val": " = 0"}, {"name": "num_codevectors_per_group", "val": " = 320"}, {"name": "num_codevector_groups", "val": " = 2"}, {"name": "contrastive_logits_temperature", "val": " = 0.1"}, {"name": "num_negatives", "val": " = 100"}, {"name": "codevector_dim", "val": " = 256"}, {"name": "proj_codevector_dim", "val": " = 256"}, {"name": "diversity_loss_weight", "val": " = 0.1"}, {"name": "ctc_loss_reduction", "val": " = 'sum'"}, {"name": "ctc_zero_infinity", "val": " = False"}, {"name": "use_weighted_layer_sum", "val": " = False"}, {"name": "classifier_proj_size", "val": " = 256"}, {"name": "tdnn_dim", "val": " = (512, 512, 512, 512, 1500)"}, {"name": "tdnn_kernel", "val": " = (5, 3, 3, 1, 1)"}, {"name": "tdnn_dilation", "val": " = (1, 2, 3, 1, 1)"}, {"name": "xvector_output_dim", "val": " = 512"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "bos_token_id", "val": " = 1"}, {"name": "eos_token_id", "val": " = 2"}, {"name": "add_adapter", "val": " = False"}, {"name": "adapter_kernel_size", "val": " = 3"}, {"name": "adapter_stride", "val": " = 2"}, {"name": "num_adapter_layers", "val": " = 3"}, {"name": "output_hidden_size", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Model) or [TFWav2Vec2Model](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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 &amp;lt;
  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 &amp;lt; 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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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`.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [Wav2Vec2Model](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.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](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) for more information.




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
```

</div>

<h2 id="transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2CTCTokenizer</name><anchor>transformers.Wav2Vec2CTCTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/tokenization_wav2vec2.py#L82</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "word_delimiter_token", "val": " = '|'"}, {"name": "do_lower_case", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  File containing the vocabulary.
- **bos_token** (`str`, *optional*, defaults to `"<s>"`) --
  The beginning of sentence token.
- **eos_token** (`str`, *optional*, defaults to `"</s>"`) --
  The end of sentence token.
- **unk_token** (`str`, *optional*, defaults to `"&amp;lt;unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **pad_token** (`str`, *optional*, defaults to `"&amp;lt;pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **word_delimiter_token** (`str`, *optional*, defaults to `"|"`) --
  The token used for defining the end of a word.
- **do_lower_case** (`bool`, *optional*, defaults to `False`) --
  Whether or not to accept lowercase input and lowercase the output when decoding.

  **kwargs --
  Additional keyword arguments passed along to [PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer)</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a Wav2Vec2CTC tokenizer.

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.PreTrainedTokenizerBase.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/tokenization_utils_base.py#L2379</source><parameters>[{"name": "text", "val": ": typing.Union[str, typing.List[str], typing.List[typing.List[str]]]"}, {"name": "text_pair", "val": ": typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None"}, {"name": "add_special_tokens", "val": ": bool = True"}, {"name": "padding", "val": ": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"}, {"name": "truncation", "val": ": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"}, {"name": "max_length", "val": ": typing.Optional[int] = None"}, {"name": "stride", "val": ": int = 0"}, {"name": "is_split_into_words", "val": ": bool = False"}, {"name": "pad_to_multiple_of", "val": ": typing.Optional[int] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"}, {"name": "return_token_type_ids", "val": ": typing.Optional[bool] = None"}, {"name": "return_attention_mask", "val": ": typing.Optional[bool] = None"}, {"name": "return_overflowing_tokens", "val": ": bool = False"}, {"name": "return_special_tokens_mask", "val": ": bool = False"}, {"name": "return_offsets_mapping", "val": ": bool = False"}, {"name": "return_length", "val": ": bool = False"}, {"name": "verbose", "val": ": bool = True"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **text** (`str`, `List[str]`, `List[List[str]]`) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
  `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
- **text_pair** (`str`, `List[str]`, `List[List[str]]`) --
  The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
  (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
  `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).

- **add_special_tokens** (`bool`, *optional*, defaults to `True`) --
  Whether or not to encode the sequences with the special tokens relative to their model.
- **padding** (`bool`, `str` or [PaddingStrategy](/docs/transformers/pr_15592/en/internal/file_utils#transformers.file_utils.PaddingStrategy), *optional*, defaults to `False`) --
  Activates and controls padding. Accepts the following values:

  - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
    sequence if provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
    acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
    lengths).
- **truncation** (`bool`, `str` or [TruncationStrategy](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy), *optional*, defaults to `False`) --
  Activates and controls truncation. Accepts the following values:

  - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
    to the maximum acceptable input length for the model if that argument is not provided. This will
    truncate token by token, removing a token from the longest sequence in the pair if a pair of
    sequences (or a batch of pairs) is provided.
  - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
    maximum acceptable input length for the model if that argument is not provided. This will only
    truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
    maximum acceptable input length for the model if that argument is not provided. This will only
    truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
    greater than the model maximum admissible input size).
- **max_length** (`int`, *optional*) --
  Controls the maximum length to use by one of the truncation/padding parameters.

  If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
  is required by one of the truncation/padding parameters. If the model has no specific maximum input
  length (like XLNet) truncation/padding to a maximum length will be deactivated.
- **stride** (`int`, *optional*, defaults to 0) --
  If set to a number along with `max_length`, the overflowing tokens returned when
  `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
  returned to provide some overlap between truncated and overflowing sequences. The value of this
  argument defines the number of overlapping tokens.
- **is_split_into_words** (`bool`, *optional*, defaults to `False`) --
  Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
  tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
  which it will tokenize. This is useful for NER or token classification.
- **pad_to_multiple_of** (`int`, *optional*) --
  If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
  the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
- **return_tensors** (`str` or [TensorType](/docs/transformers/pr_15592/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors instead of list of python integers. Acceptable values are:

  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.

- **return_token_type_ids** (`bool`, *optional*) --
  Whether to return token type IDs. If left to the default, will return the token type IDs according to
  the specific tokenizer's default, defined by the `return_outputs` attribute.

  [What are token type IDs?](../glossary#token-type-ids)
- **return_attention_mask** (`bool`, *optional*) --
  Whether to return the attention mask. If left to the default, will return the attention mask according
  to the specific tokenizer's default, defined by the `return_outputs` attribute.

  [What are attention masks?](../glossary#attention-mask)
- **return_overflowing_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
  of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
  of returning overflowing tokens.
- **return_special_tokens_mask** (`bool`, *optional*, defaults to `False`) --
  Whether or not to return special tokens mask information.
- **return_offsets_mapping** (`bool`, *optional*, defaults to `False`) --
  Whether or not to return `(char_start, char_end)` for each token.

  This is only available on fast tokenizers inheriting from [PreTrainedTokenizerFast](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast), if using
  Python's tokenizer, this method will raise `NotImplementedError`.
- **return_length**  (`bool`, *optional*, defaults to `False`) --
  Whether or not to return the lengths of the encoded inputs.
- **verbose** (`bool`, *optional*, defaults to `True`) --
  Whether or not to print more information and warnings.
  **kwargs -- passed to the `self.tokenize()` method</paramsdesc><paramgroups>0</paramgroups><rettype>[BatchEncoding](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.BatchEncoding)</rettype><retdesc>A [BatchEncoding](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields:

- **input_ids** -- List of token ids to be fed to a model.

  [What are input IDs?](../glossary#input-ids)

- **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
  if *"token_type_ids"* is in `self.model_input_names`).

  [What are token type IDs?](../glossary#token-type-ids)

- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
  `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).

  [What are attention masks?](../glossary#attention-mask)

- **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
  `return_overflowing_tokens=True`).
- **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
  `return_overflowing_tokens=True`).
- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
  regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
- **length** -- The length of the inputs (when `return_length=True`)</retdesc></docstring>

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.








</div>
<div class="docstring"></div></div>

<h2 id="transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2FeatureExtractor</name><anchor>transformers.Wav2Vec2FeatureExtractor</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py#L32</source><parameters>[{"name": "feature_size", "val": " = 1"}, {"name": "sampling_rate", "val": " = 16000"}, {"name": "padding_value", "val": " = 0.0"}, {"name": "return_attention_mask", "val": " = False"}, {"name": "do_normalize", "val": " = True"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **feature_size** (`int`, defaults to 1) --
  The feature dimension of the extracted features.
- **sampling_rate** (`int`, defaults to 16000) --
  The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
- **padding_value** (`float`, defaults to 0.0) --
  The value that is used to fill the padding values.
- **do_normalize** (`bool`, *optional*, defaults to `False`) --
  Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
  improve the performance for some models, *e.g.*,
  [wav2vec2-lv60](https://huggingface.co/models?search=lv60).
- **return_attention_mask** (`bool`, *optional*, defaults to `False`) --
  Whether or not [__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor.__call__) should return `attention_mask`.

  <Tip>

  Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using
  `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask`
  should be passed.

  For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as
  [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be
  passed for batched inference.

  </Tip></paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a Wav2Vec2 feature extractor.

This feature extractor inherits from [SequenceFeatureExtractor](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor) which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.Wav2Vec2FeatureExtractor.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py#L103</source><parameters>[{"name": "raw_speech", "val": ": typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]]"}, {"name": "padding", "val": ": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"}, {"name": "max_length", "val": ": typing.Optional[int] = None"}, {"name": "truncation", "val": ": bool = False"}, {"name": "pad_to_multiple_of", "val": ": typing.Optional[int] = None"}, {"name": "return_attention_mask", "val": ": typing.Optional[bool] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"}, {"name": "sampling_rate", "val": ": typing.Optional[int] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **raw_speech** (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`) --
  The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
  values, a list of numpy arrays or a list of list of float values.
- **padding** (`bool`, `str` or [PaddingStrategy](/docs/transformers/pr_15592/en/internal/file_utils#transformers.file_utils.PaddingStrategy), *optional*, defaults to `False`) --
  Select a strategy to pad the returned sequences (according to the model's padding side and padding
  index) among:

  - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
    sequence if provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
    acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
    lengths).
- **max_length** (`int`, *optional*) --
  Maximum length of the returned list and optionally padding length (see above).
- **truncation** (`bool`) --
  Activates truncation to cut input sequences longer than *max_length* to *max_length*.
- **pad_to_multiple_of** (`int`, *optional*) --
  If set will pad the sequence to a multiple of the provided value.

  This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
  >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
- **return_attention_mask** (`bool`, *optional*) --
  Whether to return the attention mask. If left to the default, will return the attention mask according
  to the specific feature_extractor's default.

  [What are attention masks?](../glossary#attention-mask)

  <Tip>

  Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using
  `attention_mask`. For such models, `input_values` should simply be padded with 0 and no
  `attention_mask` should be passed.

  For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as
  [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should
  be passed for batched inference.

  </Tip>

- **return_tensors** (`str` or [TensorType](/docs/transformers/pr_15592/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors instead of list of python integers. Acceptable values are:

  - `'tf'`: Return TensorFlow `tf.constant` objects.
  - `'pt'`: Return PyTorch `torch.Tensor` objects.
  - `'np'`: Return Numpy `np.ndarray` objects.
- **sampling_rate** (`int`, *optional*) --
  The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
  `sampling_rate` at the forward call to prevent silent errors.
- **padding_value** (`float`, defaults to 0.0) --</paramsdesc><paramgroups>0</paramgroups></docstring>

Main method to featurize and prepare for the model one or several sequence(s). sequences.




</div></div>

<h2 id="transformers.Wav2Vec2Processor">Wav2Vec2Processor</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2Processor</name><anchor>transformers.Wav2Vec2Processor</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L26</source><parameters>[{"name": "feature_extractor", "val": ""}, {"name": "tokenizer", "val": ""}]</parameters><paramsdesc>- **feature_extractor** (`Wav2Vec2FeatureExtractor`) --
  An instance of [Wav2Vec2FeatureExtractor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor). The feature extractor is a required input.
- **tokenizer** ([PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer)) --
  An instance of [PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer). The tokenizer is a required input.</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.

[Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) offers all the functionalities of [Wav2Vec2FeatureExtractor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor) and [PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer).
See the docstring of [__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) and [decode()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.decode) for more information.





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.Wav2Vec2Processor.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L66</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor.__call__) and returns its output. If used in the context
[as_target_processor()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.as_target_processor) this method forwards all its arguments to PreTrainedTokenizer's
[__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__). Please refer to the docstring of the above two methods for more information.


</div>
<div class="docstring">
<docstring><name>pad</name><anchor>transformers.Wav2Vec2Processor.pad</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L75</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[pad()](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad) and returns its output. If used in the context
[as_target_processor()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.as_target_processor) this method forwards all its arguments to PreTrainedTokenizer's
[pad()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.pad). Please refer to the docstring of the above two methods for more information.


</div>
<div class="docstring"></div>
<div class="docstring">
<docstring><name>save\_pretrained</name><anchor>transformers.ProcessorMixin.save_pretrained</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/processing_utils.py#L95</source><parameters>[{"name": "save_directory", "val": ""}, {"name": "push_to_hub", "val": ": bool = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **save_directory** (`str` or `os.PathLike`) --
  Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
  be created if it does not exist).
- **push_to_hub** (`bool`, *optional*, defaults to `False`) --
  Whether or not to push your processor to the Hugging Face model hub after saving it.

  <Tip warning=&amp;lcub;true}>

  Using `push_to_hub=True` will synchronize the repository you are pushing to with `save_directory`,
  which requires `save_directory` to be a local clone of the repo you are pushing to if it's an existing
  folder. Pass along `temp_dir=True` to use a temporary directory instead.

  </Tip>

  kwargs --
  Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/pr_15592/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub) method.</paramsdesc><paramgroups>0</paramgroups></docstring>

Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
can be reloaded using the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/processors#transformers.ProcessorMixin.from_pretrained) method.

<Tip>

This class method is simply calling [save_pretrained()](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) and
`save_pretrained`. Please refer to the docstrings of the methods
above for more information.

</Tip>




</div>
<div class="docstring">
<docstring><name>batch\_decode</name><anchor>transformers.Wav2Vec2Processor.batch_decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L84</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

This method forwards all its arguments to PreTrainedTokenizer's [batch_decode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode). Please
refer to the docstring of this method for more information.


</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.Wav2Vec2Processor.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L91</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

This method forwards all its arguments to PreTrainedTokenizer's [decode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode). Please refer
to the docstring of this method for more information.


</div>
<div class="docstring">
<docstring><name>as\_target\_processor</name><anchor>transformers.Wav2Vec2Processor.as_target_processor</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/processing_wav2vec2.py#L98</source><parameters>[]</parameters></docstring>

Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
Wav2Vec2.


</div></div>

<h2 id="transformers.Wav2Vec2ProcessorWithLM">Wav2Vec2ProcessorWithLM</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ProcessorWithLM</name><anchor>transformers.Wav2Vec2ProcessorWithLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L56</source><parameters>[{"name": "feature_extractor", "val": ": FeatureExtractionMixin"}, {"name": "tokenizer", "val": ": PreTrainedTokenizerBase"}, {"name": "decoder", "val": ": BeamSearchDecoderCTC"}]</parameters><paramsdesc>- **feature_extractor** ([Wav2Vec2FeatureExtractor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor)) --
  An instance of [Wav2Vec2FeatureExtractor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor). The feature extractor is a required input.
- **tokenizer** ([Wav2Vec2CTCTokenizer](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer)) --
  An instance of [Wav2Vec2CTCTokenizer](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer). The tokenizer is a required input.
- **decoder** (`pyctcdecode.BeamSearchDecoderCTC`) --
  An instance of `pyctcdecode.BeamSearchDecoderCTC`. The decoder is a required input.</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor, a Wav2Vec2 CTC tokenizer and a decoder
with language model support into a single processor for language model boosted speech recognition decoding.





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.Wav2Vec2ProcessorWithLM.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L202</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor.__call__) and returns its output. If used in the context
[as_target_processor()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM.as_target_processor) this method forwards all its arguments to
Wav2Vec2CTCTokenizer's [__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__). Please refer to the docstring of the above two
methods for more information.


</div>
<div class="docstring">
<docstring><name>pad</name><anchor>transformers.Wav2Vec2ProcessorWithLM.pad</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L212</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor's
[pad()](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad) and returns its output. If used in the context
[as_target_processor()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM.as_target_processor) this method forwards all its arguments to
Wav2Vec2CTCTokenizer's [pad()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.pad). Please refer to the docstring of the above two methods
for more information.


</div>
<div class="docstring">
<docstring><name>from\_pretrained</name><anchor>transformers.Wav2Vec2ProcessorWithLM.from_pretrained</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L100</source><parameters>[{"name": "pretrained_model_name_or_path", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **pretrained_model_name_or_path** (`str` or `os.PathLike`) --
  This can be either:

  - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
    huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
    namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
  - a path to a *directory* containing a feature extractor file saved using the
    [save_pretrained()](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g., `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON *file*, e.g.,
    `./my_model_directory/preprocessor_config.json`.
**kwargs --
Additional keyword arguments passed along to both [SequenceFeatureExtractor](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor) and
[PreTrainedTokenizer](/docs/transformers/pr_15592/en/main_classes/tokenizer#transformers.PreTrainedTokenizer)</paramsdesc><paramgroups>0</paramgroups></docstring>

Instantiate a [Wav2Vec2ProcessorWithLM](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ProcessorWithLM) from a pretrained Wav2Vec2 processor.

<Tip>

This class method is simply calling Wav2Vec2FeatureExtractor's
[from_pretrained()](/docs/transformers/pr_15592/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained), Wav2Vec2CTCTokenizer's
`from_pretrained`, and
`pyctcdecode.BeamSearchDecoderCTC.load_from_hf_hub`.

Please refer to the docstrings of the methods above for more information.

</Tip>




</div>
<div class="docstring"></div>
<div class="docstring">
<docstring><name>batch\_decode</name><anchor>transformers.Wav2Vec2ProcessorWithLM.batch_decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L222</source><parameters>[{"name": "logits", "val": ": ndarray"}, {"name": "num_processes", "val": ": typing.Optional[int] = None"}, {"name": "beam_width", "val": ": typing.Optional[int] = None"}, {"name": "beam_prune_logp", "val": ": typing.Optional[float] = None"}, {"name": "token_min_logp", "val": ": typing.Optional[float] = None"}, {"name": "hotwords", "val": ": typing.Optional[typing.Iterable[str]] = None"}, {"name": "hotword_weight", "val": ": typing.Optional[float] = None"}, {"name": "alpha", "val": ": typing.Optional[float] = None"}, {"name": "beta", "val": ": typing.Optional[float] = None"}, {"name": "unk_score_offset", "val": ": typing.Optional[float] = None"}, {"name": "lm_score_boundary", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **logits** (`np.ndarray`) --
  The logits output vector of the model representing the log probabilities for each token.
- **num_processes** (`int`, *optional*) --
  Number of processes on which the function should be parallelized over. Defaults to the number of
  available CPUs.
- **beam_width** (`int`, *optional*) --
  Maximum number of beams at each step in decoding. Defaults to pyctcdecode's DEFAULT_BEAM_WIDTH.
- **beam_prune_logp** (`int`, *optional*) --
  Beams that are much worse than best beam will be pruned Defaults to pyctcdecode's DEFAULT_PRUNE_LOGP.
- **token_min_logp** (`int`, *optional*) --
  Tokens below this logp are skipped unless they are argmax of frame Defaults to pyctcdecode's
  DEFAULT_MIN_TOKEN_LOGP.
- **hotwords** (`List[str]`, *optional*) --
  List of words with extra importance, can be OOV for LM
- **hotword_weight** (`int`, *optional*) --
  Weight factor for hotword importance Defaults to pyctcdecode's DEFAULT_HOTWORD_WEIGHT.
- **alpha** (`float`, *optional*) --
  Weight for language model during shallow fusion
- **beta** (`float`, *optional*) --
  Weight for length score adjustment of during scoring
- **unk_score_offset** (`float`, *optional*) --
  Amount of log score offset for unknown tokens
- **lm_score_boundary** (`bool`, *optional*) --
  Whether to have kenlm respect boundaries when scoring</paramsdesc><paramgroups>0</paramgroups><retdesc>`Wav2Vec2DecoderWithLMOutput` or `tuple`.</retdesc></docstring>

Batch decode output logits to audio transcription with language model support.

<Tip>

This function makes use of Python's multiprocessing.

</Tip>






</div>
<div class="docstring">
<docstring><name>decode</name><anchor>transformers.Wav2Vec2ProcessorWithLM.decode</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L321</source><parameters>[{"name": "logits", "val": ": ndarray"}, {"name": "beam_width", "val": ": typing.Optional[int] = None"}, {"name": "beam_prune_logp", "val": ": typing.Optional[float] = None"}, {"name": "token_min_logp", "val": ": typing.Optional[float] = None"}, {"name": "hotwords", "val": ": typing.Optional[typing.Iterable[str]] = None"}, {"name": "hotword_weight", "val": ": typing.Optional[float] = None"}, {"name": "alpha", "val": ": typing.Optional[float] = None"}, {"name": "beta", "val": ": typing.Optional[float] = None"}, {"name": "unk_score_offset", "val": ": typing.Optional[float] = None"}, {"name": "lm_score_boundary", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **logits** (`np.ndarray`) --
  The logits output vector of the model representing the log probabilities for each token.
- **beam_width** (`int`, *optional*) --
  Maximum number of beams at each step in decoding. Defaults to pyctcdecode's DEFAULT_BEAM_WIDTH.
- **beam_prune_logp** (`int`, *optional*) --
  A threshold to prune beams with log-probs less than best_beam_logp + beam_prune_logp. The value should
  be &amp;lt;= 0. Defaults to pyctcdecode's DEFAULT_PRUNE_LOGP.
- **token_min_logp** (`int`, *optional*) --
  Tokens with log-probs below token_min_logp are skipped unless they are have the maximum log-prob for an
  utterance. Defaults to pyctcdecode's DEFAULT_MIN_TOKEN_LOGP.
- **hotwords** (`List[str]`, *optional*) --
  List of words with extra importance which can be missing from the LM's vocabulary, e.g. ["huggingface"]
- **hotword_weight** (`int`, *optional*) --
  Weight multiplier that boosts hotword scores. Defaults to pyctcdecode's DEFAULT_HOTWORD_WEIGHT.
- **alpha** (`float`, *optional*) --
  Weight for language model during shallow fusion
- **beta** (`float`, *optional*) --
  Weight for length score adjustment of during scoring
- **unk_score_offset** (`float`, *optional*) --
  Amount of log score offset for unknown tokens
- **lm_score_boundary** (`bool`, *optional*) --
  Whether to have kenlm respect boundaries when scoring</paramsdesc><paramgroups>0</paramgroups><retdesc>`Wav2Vec2DecoderWithLMOutput` or `tuple`.</retdesc></docstring>

Decode output logits to audio transcription with language model support.






</div>
<div class="docstring">
<docstring><name>as\_target\_processor</name><anchor>transformers.Wav2Vec2ProcessorWithLM.as_target_processor</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L398</source><parameters>[]</parameters></docstring>

Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
Wav2Vec2.


</div></div>

<h2 id="transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput">Wav2Vec2 specific outputs</h2>

<div class="docstring">

<docstring><name>class transformers.models.wav2vec2\_with\_lm.processing\_wav2vec2\_with\_lm.Wav2Vec2DecoderWithLMOutput</name><anchor>transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py#L38</source><parameters>[{"name": "text", "val": ": typing.Union[typing.List[str], str]"}, {"name": "logit_score", "val": ": typing.Union[typing.List[float], float] = None"}, {"name": "lm_score", "val": ": typing.Union[typing.List[float], float] = None"}]</parameters><paramsdesc>- **text** (list of `str`) --
  Decoded logits in text from. Usually the speech transcription.
- **logit_score** (list of `float`) --
  Total logit score of the beam associated with produced text.
- **lm_score** (list of `float`) --
  Fused lm_score of the beam associated with produced text.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of `Wav2Vec2DecoderWithLM`, with transcription.




</div>

<div class="docstring">

<docstring><name>class transformers.models.wav2vec2.modeling\_wav2vec2.Wav2Vec2BaseModelOutput</name><anchor>transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L92</source><parameters>[{"name": "last_hidden_state", "val": ": FloatTensor = None"}, {"name": "extract_features", "val": ": FloatTensor = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) --
  Sequence of hidden-states at the output of the last layer of the model.
- **extract_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`) --
  Sequence of extracted feature vectors of the last convolutional layer of the model.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) --
  Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of `Wav2Vec2BaseModelOutput`, with potential hidden states and attentions.




</div>

<div class="docstring">

<docstring><name>class transformers.models.wav2vec2.modeling\_wav2vec2.Wav2Vec2ForPreTrainingOutput</name><anchor>transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L121</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "projected_states", "val": ": FloatTensor = None"}, {"name": "projected_quantized_states", "val": ": FloatTensor = None"}, {"name": "codevector_perplexity", "val": ": FloatTensor = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "contrastive_loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "diversity_loss", "val": ": typing.Optional[torch.FloatTensor] = None"}]</parameters><paramsdesc>- **loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) --
  Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
  paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
- **projected_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) --
  Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
  projected quantized states.
- **projected_quantized_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) --
  Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
  target vectors for contrastive loss.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) --
  Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **contrastive_loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) --
  The contrastive loss (L_m) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
- **diversity_loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) --
  The diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of [Wav2Vec2ForPreTraining](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining), with potential hidden states and attentions.




</div>

<div class="docstring">

<docstring><name>class transformers.models.wav2vec2.modeling\_flax\_wav2vec2.FlaxWav2Vec2BaseModelOutput</name><anchor>transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L46</source><parameters>[{"name": "last_hidden_state", "val": ": ndarray = None"}, {"name": "extract_features", "val": ": ndarray = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None"}]</parameters><paramsdesc>- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) --
  Sequence of hidden-states at the output of the last layer of the model.
- **extract_features** (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`) --
  Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim`
  being the dimension of the last convolutional layer.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) --
  Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of `FlaxWav2Vec2BaseModelOutput`, with potential hidden states and attentions.





<div class="docstring">
<docstring><name>replace</name><anchor>None</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/flax/struct.py#L120</source><parameters>[{"name": "**updates", "val": ""}]</parameters></docstring>
"Returns a new object replacing the specified fields with new values.

</div></div>

<div class="docstring">

<docstring><name>class transformers.models.wav2vec2.modeling\_flax\_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput</name><anchor>transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L76</source><parameters>[{"name": "projected_states", "val": ": ndarray = None"}, {"name": "projected_quantized_states", "val": ": ndarray = None"}, {"name": "codevector_perplexity", "val": ": ndarray = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None"}]</parameters><paramsdesc>- **loss** (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`) --
  Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
  paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
- **projected_states** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) --
  Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
  projected quantized states.
- **projected_quantized_states** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) --
  Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
  target vectors for contrastive loss.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) --
  Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output type of `FlaxWav2Vec2ForPreTrainingOutput`, with potential hidden states and attentions.





<div class="docstring">
<docstring><name>replace</name><anchor>None</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/flax/struct.py#L120</source><parameters>[{"name": "**updates", "val": ""}]</parameters></docstring>
"Returns a new object replacing the specified fields with new values.

</div></div>

<h2 id="transformers.Wav2Vec2Model">Wav2Vec2Model</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2Model</name><anchor>transformers.Wav2Vec2Model</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1237</source><parameters>[{"name": "config", "val": ": Wav2Vec2Config"}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2Model.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1323</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "mask_time_indices", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **extract_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`) -- Sequence of extracted feature vectors of the last convolutional layer of the model.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [Wav2Vec2Model](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Model) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")

>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 292, 768]
```


</div></div>

<h2 id="transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ForCTC</name><anchor>transformers.Wav2Vec2ForCTC</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1691</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2ForCTC.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1732</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "labels", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*) --
  Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
  the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
  All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
  config.vocab_size - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.CausalLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.CausalLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.CausalLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [Wav2Vec2ForCTC](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import torch

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)

>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
```

```python
>>> with processor.as_target_processor():
...     inputs["labels"] = processor(dataset[0]["text"], return_tensors="pt").input_ids

>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
53.48
```


</div></div>

<h2 id="transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ForSequenceClassification</name><anchor>transformers.Wav2Vec2ForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1821</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.

Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2ForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1866</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "labels", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [Wav2Vec2ForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
>>> from datasets import load_dataset
>>> import torch

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
>>> model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")

>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'_unknown_'
```

```python
>>> # compute loss - target_label is e.g. "down"
>>> target_label = model.config.id2label[0]
>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
6.54
```


</div></div>

<h2 id="transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ForAudioFrameClassification</name><anchor>transformers.Wav2Vec2ForAudioFrameClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1944</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization.

Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2ForAudioFrameClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1987</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [Wav2Vec2ForAudioFrameClassification](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForAudioFrameClassification
>>> from datasets import load_dataset
>>> import torch

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sd")
>>> model = Wav2Vec2ForAudioFrameClassification.from_pretrained("anton-l/wav2vec2-base-superb-sd")

>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> probabilities = torch.sigmoid(logits[0])
>>> # labels is a one-hot array of shape (num_frames, num_speakers)
>>> labels = (probabilities > 0.5).long()
>>> labels[0].tolist()
[0, 0]
```


</div></div>

<h2 id="transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ForXVector</name><anchor>transformers.Wav2Vec2ForXVector</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L2099</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

Wav2Vec2 Model with an XVector feature extraction head on top for tasks like Speaker Verification.

Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2ForXVector.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L2161</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "labels", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>`transformers.models.wav2vec2.modeling_wav2vec2.XVectorOutput`or `tuple(torch.FloatTensor)`</rettype><retdesc>A `transformers.models.wav2vec2.modeling_wav2vec2.XVectorOutput`or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`) -- Classification hidden states before AMSoftmax.
- **embeddings** (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`) -- Utterance embeddings used for vector similarity-based retrieval.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [Wav2Vec2ForXVector](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForXVector
>>> from datasets import load_dataset
>>> import torch

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sv")
>>> model = Wav2Vec2ForXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv")

>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(
...     [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
... )
>>> with torch.no_grad():
...     embeddings = model(**inputs).embeddings

>>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()

>>> # the resulting embeddings can be used for cosine similarity-based retrieval
>>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
>>> similarity = cosine_sim(embeddings[0], embeddings[1])
>>> threshold = 0.7  # the optimal threshold is dataset-dependent
>>> if similarity &amp;lt; threshold:
...     print("Speakers are not the same!")
>>> round(similarity.item(), 2)
0.98
```


</div></div>

<h2 id="transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</h2>

<div class="docstring">

<docstring><name>class transformers.Wav2Vec2ForPreTraining</name><anchor>transformers.Wav2Vec2ForPreTraining</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1386</source><parameters>[{"name": "config", "val": ": Wav2Vec2Config"}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
Wav2Vec2 Model with a quantizer and `VQ` head on top.
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.Wav2Vec2ForPreTraining.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1447</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "mask_time_indices", "val": " = None"}, {"name": "sampled_negative_indices", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_values** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *torch.FloatTensor*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)

  <Tip warning=&amp;lcub;true}>

  `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
  True`. For all models whose processor has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.

  </Tip>

- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **mask_time_indices** (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
  masked extracted features in *config.proj_codevector_dim* space.
- **sampled_negative_indices** (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*) --
  Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
  Required input for pre-training.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) -- Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
  paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
- **projected_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) -- Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
  projected quantized states.
- **projected_quantized_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) -- Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
  target vectors for contrastive loss.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **contrastive_loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) -- The contrastive loss (L_m) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
- **diversity_loss** (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`) -- The diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .</retdesc></docstring>
The [Wav2Vec2ForPreTraining](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> import torch
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("patrickvonplaten/wav2vec2-base")
>>> model = Wav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = feature_extractor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1

>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
>>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)
>>> mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long)

>>> with torch.no_grad():
...     outputs = model(input_values, mask_time_indices=mask_time_indices)

>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)

>>> # show that cosine similarity is much higher than random
>>> cosine_sim[mask_time_indices.to(torch.bool)].mean() > 0.5
tensor(True)

>>> # for contrastive loss training model should be put into train mode
>>> model = model.train()
>>> loss = model(input_values, mask_time_indices=mask_time_indices).loss
```

</div></div>

<h2 id="transformers.TFWav2Vec2Model">TFWav2Vec2Model</h2>

<div class="docstring">

<docstring><name>class transformers.TFWav2Vec2Model</name><anchor>transformers.TFWav2Vec2Model</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1428</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare TFWav2Vec2 Model transformer outputing raw hidden-states without any specific head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :

- a single Tensor with `input_values` only and nothing else: `model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model(&amp;lcub;"input_values": input_values, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFWav2Vec2Model.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1434</source><parameters>[{"name": "input_values", "val": ": Tensor"}, {"name": "attention_mask", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "token_type_ids", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "head_mask", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "training", "val": ": bool = False"}]</parameters><paramsdesc>- **input_values** (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(&amp;lcub;0})`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0}, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation.
  This is useful if you want more control over how to convert `input_values` indices into associated vectors
  than the model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFBaseModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **hidden_states** (`tuple(tf.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [TFWav2Vec2Model](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2Processor, TFWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```

</div></div>

<h2 id="transformers.TFWav2Vec2ForCTC">TFWav2Vec2ForCTC</h2>

<div class="docstring">

<docstring><name>class transformers.TFWav2Vec2ForCTC</name><anchor>transformers.TFWav2Vec2ForCTC</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1531</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).

This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :

- a single Tensor with `input_values` only and nothing else: `model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model(&amp;lcub;"input_values": input_values, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFWav2Vec2ForCTC.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1558</source><parameters>[{"name": "input_values", "val": ": Tensor"}, {"name": "attention_mask", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "token_type_ids", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "head_mask", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}]</parameters><paramsdesc>- **input_values** (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(&amp;lcub;0})`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **token_type_ids** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`np.ndarray` or `tf.Tensor` of shape `(&amp;lcub;0}, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation.
  This is useful if you want more control over how to convert `input_values` indices into associated vectors
  than the model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False``) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_values` docstring) Tokens with indices set to `-100` are ignored (masked),
  the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFCausalLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFCausalLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) and inputs.

- **loss** (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided) -- Language modeling loss (for next-token prediction).
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [TFWav2Vec2ForCTC](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForCTC) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> import tensorflow as tf
>>> from transformers import Wav2Vec2Processor, TFWav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = tf.argmax(logits, axis=-1)

>>> transcription = processor.decode(predicted_ids[0])

>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"

>>> # wrap processor as target processor to encode labels
>>> with processor.as_target_processor():
...     labels = processor(transcription, return_tensors="tf").input_ids

>>> loss = model(input_values, labels=labels).loss
```

</div></div>

<h2 id="transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxWav2Vec2Model</name><anchor>transformers.FlaxWav2Vec2Model</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L1020</source><parameters>[{"name": "config", "val": ": Wav2Vec2Config"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1024)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxWav2Vec2PreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L867</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "mask_time_indices", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **input_values** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *jnp.ndarray*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed
  if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor
  has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.
- **mask_time_indices** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
  masked extracted features in *config.proj_codevector_dim* space.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>`) and inputs.

- **last_hidden_state** (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **extract_features** (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`) -- Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim`
  being the dimension of the last convolutional layer.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The `FlaxWav2Vec2PreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(
...     ds["speech"][0], sampling_rate=16_000, return_tensors="np"
>>> ).input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```


</div></div>

<h2 id="transformers.FlaxWav2Vec2ForCTC">FlaxWav2Vec2ForCTC</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxWav2Vec2ForCTC</name><anchor>transformers.FlaxWav2Vec2ForCTC</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L1136</source><parameters>[{"name": "config", "val": ": Wav2Vec2Config"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1024)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxWav2Vec2PreTrainedModel.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L867</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "mask_time_indices", "val": " = None"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **input_values** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *jnp.ndarray*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed
  if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor
  has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.
- **mask_time_indices** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
  masked extracted features in *config.proj_codevector_dim* space.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_flax_outputs.FlaxMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_flax_outputs.FlaxMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>`) and inputs.

- **logits** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The `FlaxWav2Vec2PreTrainedModel`forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> import jax.numpy as jnp
>>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60")
>>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(
...     ds["speech"][0], sampling_rate=16_000, return_tensors="np"
>>> ).input_values  # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = jnp.argmax(logits, axis=-1)

>>> transcription = processor.decode(predicted_ids[0])
>>> # should give:  "A MAN SAID TO THE UNIVERSE SIR I EXIST"
```


</div></div>

<h2 id="transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</h2>

<div class="docstring">

<docstring><name>class transformers.FlaxWav2Vec2ForPreTraining</name><anchor>transformers.FlaxWav2Vec2ForPreTraining</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L1276</source><parameters>[{"name": "config", "val": ": Wav2Vec2Config"}, {"name": "input_shape", "val": ": typing.Tuple = (1, 1024)"}, {"name": "seed", "val": ": int = 0"}, {"name": "dtype", "val": ": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([Wav2Vec2Config](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Config)) -- Model configuration class with all the parameters of the model.
  Initializing with a config file does not load the weights associated with the model, only the
  configuration. Check out the [from_pretrained()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
- **dtype** (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`) --
  The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
  `jax.numpy.bfloat16` (on TPUs).

  This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
  specified all the computation will be performed with the given `dtype`.

  **Note that this only specifies the dtype of the computation and does not influence the dtype of model
  parameters.**

  If you wish to change the dtype of the model parameters, see [to_fp16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and
  [to_bf16()](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).</paramsdesc><paramgroups>0</paramgroups></docstring>
Wav2Vec2 Model with a quantizer and `VQ` head on top.
Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.

This model inherits from [FlaxPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)





<div class="docstring">
<docstring><name>\_\_call\_\_</name><anchor>transformers.FlaxWav2Vec2ForPreTraining.__call__</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py#L1279</source><parameters>[{"name": "input_values", "val": ""}, {"name": "attention_mask", "val": " = None"}, {"name": "mask_time_indices", "val": " = None"}, {"name": "gumbel_temperature", "val": ": int = 1"}, {"name": "params", "val": ": dict = None"}, {"name": "dropout_rng", "val": ": PRNGKey = None"}, {"name": "gumbel_rng", "val": ": PRNGKey = None"}, {"name": "train", "val": ": bool = False"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **input_values** (`jnp.ndarray` of shape `(batch_size, sequence_length)`) --
  Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
  into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
  soundfile*). To prepare the array into *input_values*, the [Wav2Vec2Processor](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor) should be used for padding
  and conversion into a tensor of type *jnp.ndarray*. See [Wav2Vec2Processor.__call__()](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor.__call__) for details.
- **attention_mask** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
  1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed
  if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor
  has `config.return_attention_mask == False`, such as
  [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be
  passed to avoid degraded performance when doing batched inference. For such models `input_values` should
  simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly
  different results depending on whether `input_values` is padded or not.
- **mask_time_indices** (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
  masked extracted features in *config.proj_codevector_dim* space.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration (`&amp;lt;class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>`) and inputs.

- **loss** (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`) -- Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
  paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
- **projected_states** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) -- Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
  projected quantized states.
- **projected_quantized_states** (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`) -- Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
  target vectors for contrastive loss.
- **hidden_states** (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [FlaxWav2Vec2ForPreTraining](/docs/transformers/pr_15592/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining) forward method, overrides the `__call__` special method.

<Tip>

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

</Tip>







Example:

```python
>>> import optax
>>> import numpy as np
>>> import jax.numpy as jnp
>>> from transformers import Wav2Vec2FeatureExtractor, FlaxWav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1

>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
>>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)

>>> outputs = model(input_values, mask_time_indices=mask_time_indices)

>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states)

>>> # show that cosine similarity is much higher than random
>>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5
```


</div></div>
