Adding TFWav2Vec2Model (#11617)

* [WIP] Add TFWav2Vec2Model

Work in progress for adding a tensorflow version of Wav2Vec2

* feedback changes

* small fix

* Test Feedback Round 1

* Add SpecAugment and CTC Loss

* correct spec augment mask creation

* docstring and correct copyright

* correct bugs

* remove bogus file

* finish tests correction

* del unnecessary layers

* Update src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style

* correct final bug

* Feedback Changes
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/index.rst

@@ -399,7 +399,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/wav2vec2.rst

@@ -80,9 +80,22 @@ Wav2Vec2ForCTC
 .. autoclass:: transformers.Wav2Vec2ForCTC
     :members: forward
 
-
 Wav2Vec2ForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.Wav2Vec2ForPreTraining
     :members: forward
+
+
+TFWav2Vec2Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFWav2Vec2Model
+    :members: call
+
+
+TFWav2Vec2ForCTC
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFWav2Vec2ForCTC
+    :members: call

src/transformers/__init__.py

@@ -1430,6 +1430,14 @@ if is_tf_available():
             "TFTransfoXLPreTrainedModel",
         ]
     )
+    _import_structure["models.wav2vec2"].extend(
+        [
+            "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFWav2Vec2ForCTC",
+            "TFWav2Vec2Model",
+            "TFWav2Vec2PreTrainedModel",
+        ]
+    )
     _import_structure["models.xlm"].extend(
         [
             "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2743,6 +2751,12 @@ if TYPE_CHECKING:
             TFTransfoXLModel,
             TFTransfoXLPreTrainedModel,
         )
+        from .models.wav2vec2 import (
+            TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFWav2Vec2ForCTC,
+            TFWav2Vec2Model,
+            TFWav2Vec2PreTrainedModel,
+        )
         from .models.xlm import (
             TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFXLMForMultipleChoice,

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -37,6 +37,7 @@ from . import (
     ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WEIGHTS_NAME,
     XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -79,10 +80,13 @@ from . import (
     TFRobertaForSequenceClassification,
     TFT5ForConditionalGeneration,
     TFTransfoXLLMHeadModel,
+    TFWav2Vec2Model,
     TFXLMRobertaForMaskedLM,
     TFXLMWithLMHeadModel,
     TFXLNetLMHeadModel,
     TransfoXLConfig,
+    Wav2Vec2Config,
+    Wav2Vec2Model,
     XLMConfig,
     XLMRobertaConfig,
     XLNetConfig,
@@ -287,6 +291,12 @@ MODEL_CLASSES = {
         ElectraForPreTraining,
         ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ),
+    "wav2vec2": (
+        Wav2Vec2Config,
+        TFWav2Vec2Model,
+        Wav2Vec2Model,
+        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    ),
 }
 
 

src/transformers/models/auto/modeling_tf_auto.py

@@ -163,6 +163,7 @@ from ..transfo_xl.modeling_tf_transfo_xl import (
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )
+from ..wav2vec2.modeling_tf_wav2vec2 import TFWav2Vec2Model
 from ..xlm.modeling_tf_xlm import (
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
@@ -218,6 +219,7 @@ from .configuration_auto import (
     RoFormerConfig,
     T5Config,
     TransfoXLConfig,
+    Wav2Vec2Config,
     XLMConfig,
     XLMRobertaConfig,
     XLNetConfig,
@@ -263,6 +265,7 @@ TF_MODEL_MAPPING = OrderedDict(
         (PegasusConfig, TFPegasusModel),
         (BlenderbotConfig, TFBlenderbotModel),
         (BlenderbotSmallConfig, TFBlenderbotSmallModel),
+        (Wav2Vec2Config, TFWav2Vec2Model),
     ]
 )
 

src/transformers/models/wav2vec2/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _BaseLazyModule, is_tokenizers_available, is_torch_available
+from ...file_utils import _BaseLazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -38,6 +38,15 @@ if is_torch_available():
     ]
 
 
+if is_tf_available():
+    _import_structure["modeling_tf_wav2vec2"] = [
+        "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFWav2Vec2ForCTC",
+        "TFWav2Vec2Model",
+        "TFWav2Vec2PreTrainedModel",
+    ]
+
+
 if TYPE_CHECKING:
     from .configuration_wav2vec2 import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2Config
     from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
@@ -54,6 +63,14 @@ if TYPE_CHECKING:
             Wav2Vec2PreTrainedModel,
         )
 
+    if is_tf_available():
+        from .modeling_tf_wav2vec2 import (
+            TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFWav2Vec2ForCTC,
+            TFWav2Vec2Model,
+            TFWav2Vec2PreTrainedModel,
+        )
+
 
 else:
     import importlib

src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py

+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" TensorFlow Wav2Vec2 model. """
+
+import inspect
+import warnings
+from typing import Any, Dict, Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
+from ...modeling_tf_utils import (
+    TFPreTrainedModel,
+    booleans_processing,
+    get_initializer,
+    keras_serializable,
+    shape_list,
+)
+from ...tokenization_utils_base import BatchEncoding
+from ...utils import logging
+from .configuration_wav2vec2 import Wav2Vec2Config
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
+_CONFIG_FOR_DOC = "Wav2Vec2Config"
+_TOKENIZER_FOR_DOC = "Wav2Vec2Tokenizer"
+
+TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "facebook/wav2vec2-base-960h",
+    "facebook/wav2vec2-large-960h",
+    "facebook/wav2vec2-large-960h-lv60",
+    "facebook/wav2vec2-large-960h-lv60-self",
+    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
+]
+
+LARGE_NEGATIVE = -1e8
+
+
+def input_values_processing(func, config, input_values, **kwargs):
+    """
+    Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
+    has to be named accordingly to the parameters name, i.e. :obj:`input_values = tf.keras.Input(shape=(128,),
+    dtype='float32', name="input_values")` otherwise the order of the tensors will not be guaranteed during the
+    training.
+
+    Args:
+        func (:obj:`callable`):
+            The callable function of the TensorFlow model.
+        config (:class:`~transformers.PretrainedConfig`):
+            The config of the running model.
+        **kwargs:
+            The inputs of the model.
+
+    Returns:
+        Two lists, one for the missing layers, and another one for the unexpected layers.
+    """
+    signature = dict(inspect.signature(func).parameters)
+    signature.pop("kwargs", None)
+    signature.pop("self", None)
+    parameter_names = list(signature.keys())
+    output = {}
+    allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)
+
+    if len(kwargs["kwargs_call"]) > 0:
+        raise ValueError(
+            f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}."
+        )
+
+    kwargs.pop("kwargs_call")
+
+    for k, v in kwargs.items():
+        if isinstance(v, allowed_types) or v is None:
+            output[k] = v
+        else:
+            raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
+
+    if isinstance(input_values, (tuple, list)):
+        for i, input in enumerate(input_values):
+            # EagerTensors don't allow to use the .name property so we check for a real Tensor
+            if type(input) == tf.Tensor:
+                # Tensor names have always the pattern `name:id` then we check only the
+                # `name` part
+                tensor_name = input.name.split(":")[0]
+
+                if tensor_name in parameter_names:
+                    output[tensor_name] = input
+                else:
+                    output[parameter_names[i]] = input
+            elif isinstance(input, allowed_types) or input is None:
+                output[parameter_names[i]] = input
+            else:
+                raise ValueError(
+                    f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for {parameter_names[i]}."
+                )
+    elif isinstance(input_values, (dict, BatchEncoding)):
+        if "inputs" in input_values:
+            warnings.warn(
+                "The `inputs` argument is deprecated and will be removed in a future version, use `input_values` instead.",
+                FutureWarning,
+            )
+
+            output["input_values"] = input_values.pop("inputs")
+
+        if "decoder_cached_states" in input_values:
+            warnings.warn(
+                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
+                FutureWarning,
+            )
+            output["past_key_values"] = input_values.pop("decoder_cached_states")
+
+        for k, v in dict(input_values).items():
+            if isinstance(v, allowed_types) or v is None:
+                output[k] = v
+            elif k not in parameter_names and "args" not in parameter_names:
+                logger.warning(
+                    f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored."
+                )
+                continue
+            else:
+                raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
+    else:
+        if isinstance(input_values, tf.Tensor) or input_values is None:
+            output[parameter_names[0]] = input_values
+        else:
+            raise ValueError(
+                f"Data of type {type(input_values)} is not allowed only {allowed_types} is accepted for {parameter_names[0]}."
+            )
+
+    for name in parameter_names:
+        if name not in list(output.keys()) and name != "args":
+            output[name] = kwargs.pop(name, signature[name].default)
+
+    # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
+    # So to respect the proper output we have to add this exception
+    if "args" in output:
+        if output["args"] is not None and type(output["args"]) == tf.Tensor:
+            tensor_name = output["args"].name.split(":")[0]
+            output[tensor_name] = output["args"]
+        else:
+            # `args` in this case is always the first parameter, then `input_values`
+            output["input_values"] = output["args"]
+
+        del output["args"]
+
+    if "kwargs" in output:
+        del output["kwargs"]
+
+    boolean_dict = {
+        k: v
+        for k, v in output.items()
+        if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"]
+    }
+
+    output.update(booleans_processing(config=config, **boolean_dict))
+
+    return output
+
+
+def _sample_without_replacement(distribution, num_samples):
+    """
+    Categorical sampling without replacement is currently not implemented. The gumbel-max trick will do for now - see
+    https://github.com/tensorflow/tensorflow/issues/9260 for more info
+    """
+    z = -tf.math.log(tf.random.uniform(shape_list(distribution), 0, 1))
+    _, indices = tf.nn.top_k(distribution + z, num_samples)
+    return indices
+
+
+def _scatter_values_on_batch_indices(values, batch_indices, output_shape):
+    """
+    Scatter function as in PyTorch with indices in format (batch_dim, indixes)
+    """
+    indices_shape = shape_list(batch_indices)
+    # broadcast batch dim to indices_shape
+    broad_casted_batch_dims = tf.reshape(
+        tf.broadcast_to(tf.expand_dims(tf.range(indices_shape[0]), axis=-1), indices_shape), [1, -1]
+    )
+    # transform batch_indices to pair_indices
+    pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
+    # scatter values to pair indices
+    return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), output_shape)
+
+
+def _compute_mask_indices(
+    shape: Tuple[int, int],
+    mask_prob: float,
+    mask_length: int,
+    min_masks: int = 0,
+) -> tf.Tensor:
+    """
+    Computes random mask spans for a given shape
+
+    Args:
+        shape: the the shape for which to compute masks.
+            should be of size 2 where first element is batch size and 2nd is timesteps
+        attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
+        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
+        mask_length: size of the mask
+        min_masks: minimum number of masked spans
+
+    Adapted from `fairseq's data_utils.py
+    <https://github.com/pytorch/fairseq/blob/e0788f7007a8473a76db573985031f3c94201e79/fairseq/data/data_utils.py#L376>`__.
+    """
+    batch_size, sequence_length = shape
+
+    if mask_length < 1:
+        raise ValueError("`mask_length` has to be bigger than 0.")
+
+    if mask_length > sequence_length:
+        raise ValueError(
+            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
+        )
+    # compute number of masked spans in batch
+    num_masked_spans = int(mask_prob * sequence_length / mask_length + tf.random.uniform((1,)))
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # make sure num masked indices <= sequence_length
+    if num_masked_spans * mask_length > sequence_length:
+        num_masked_spans = sequence_length // mask_length
+
+    # SpecAugment mask to fill
+    spec_aug_mask = tf.zeros((batch_size, sequence_length), dtype=tf.int32)
+
+    # uniform distribution to sample from, make sure that offset samples are < sequence_length
+    uniform_dist = tf.ones((batch_size, sequence_length - (mask_length - 1)))
+
+    # get random indices to mask
+    spec_aug_mask_idxs = _sample_without_replacement(uniform_dist, num_masked_spans)
+
+    # expand masked indices to masked spans
+    spec_aug_mask_idxs = tf.expand_dims(spec_aug_mask_idxs, -1)
+    spec_aug_mask_idxs = tf.tile(spec_aug_mask_idxs, (1, 1, mask_length))
+    spec_aug_mask_idxs = tf.reshape(spec_aug_mask_idxs, (batch_size, num_masked_spans * mask_length))
+
+    offsets = tf.range(mask_length)[tf.newaxis, tf.newaxis, :]
+    offsets = tf.tile(offsets, (batch_size, num_masked_spans, 1))
+    offsets = tf.reshape(offsets, (batch_size, num_masked_spans * mask_length))
+
+    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
+
+    # scatter indices to mask
+    spec_aug_mask = _scatter_values_on_batch_indices(
+        tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, spec_aug_mask.shape
+    )
+
+    return tf.cast(spec_aug_mask, tf.float32)
+
+
+def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    src_len = shape_list(mask)[1]
+    tgt_len = tgt_len if tgt_len is not None else src_len
+    one_cst = tf.constant(1.0)
+    mask = tf.cast(mask, dtype=one_cst.dtype)
+    expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+    return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+class TFWav2Vec2GroupNorm(tf.keras.layers.Layer):
+    """
+    From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization
+    """
+
+    def __init__(
+        self,
+        groups: int = 32,
+        axis: int = -1,
+        epsilon: float = 1e-3,
+        center: bool = True,
+        scale: bool = True,
+        beta_initializer: tf.keras.initializers.Initializer = "zeros",
+        gamma_initializer: tf.keras.initializers.Initializer = "ones",
+        beta_regularizer: tf.keras.regularizers.Regularizer = None,
+        gamma_regularizer: tf.keras.regularizers.Regularizer = None,
+        beta_constraint: tf.keras.constraints.Constraint = None,
+        gamma_constraint: tf.keras.constraints.Constraint = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.supports_masking = True
+        self.groups = groups
+        self.axis = axis
+        self.epsilon = epsilon
+        self.center = center
+        self.scale = scale
+        self.beta_initializer = tf.keras.initializers.get(beta_initializer)
+        self.gamma_initializer = tf.keras.initializers.get(gamma_initializer)
+        self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer)
+        self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer)
+        self.beta_constraint = tf.keras.constraints.get(beta_constraint)
+        self.gamma_constraint = tf.keras.constraints.get(gamma_constraint)
+        self._check_axis()
+
+    def build(self, input_shape):
+
+        self._check_if_input_shape_is_none(input_shape)
+        self._set_number_of_groups_for_instance_norm(input_shape)
+        self._check_size_of_dimensions(input_shape)
+        self._create_input_spec(input_shape)
+
+        self._add_gamma_weight(input_shape)
+        self._add_beta_weight(input_shape)
+        self.built = True
+        super().build(input_shape)
+
+    def call(self, inputs):
+
+        input_shape = tf.keras.backend.int_shape(inputs)
+        tensor_input_shape = tf.shape(inputs)
+
+        reshaped_inputs, group_shape = self._reshape_into_groups(inputs, input_shape, tensor_input_shape)
+
+        normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape)
+
+        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
+        if not is_instance_norm:
+            outputs = tf.reshape(normalized_inputs, tensor_input_shape)
+        else:
+            outputs = normalized_inputs
+
+        return outputs
+
+    def get_config(self):
+        config = {
+            "groups": self.groups,
+            "axis": self.axis,
+            "epsilon": self.epsilon,
+            "center": self.center,
+            "scale": self.scale,
+            "beta_initializer": tf.keras.initializers.serialize(self.beta_initializer),
+            "gamma_initializer": tf.keras.initializers.serialize(self.gamma_initializer),
+            "beta_regularizer": tf.keras.regularizers.serialize(self.beta_regularizer),
+            "gamma_regularizer": tf.keras.regularizers.serialize(self.gamma_regularizer),
+            "beta_constraint": tf.keras.constraints.serialize(self.beta_constraint),
+            "gamma_constraint": tf.keras.constraints.serialize(self.gamma_constraint),
+        }
+        base_config = super().get_config()
+        return {**base_config, **config}
+
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
+    def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
+
+        group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
+        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
+        if not is_instance_norm:
+            group_shape[self.axis] = input_shape[self.axis] // self.groups
+            group_shape.insert(self.axis, self.groups)
+            group_shape = tf.stack(group_shape)
+            reshaped_inputs = tf.reshape(inputs, group_shape)
+            return reshaped_inputs, group_shape
+        else:
+            return inputs, group_shape
+
+    def _apply_normalization(self, reshaped_inputs, input_shape):
+
+        group_shape = tf.keras.backend.int_shape(reshaped_inputs)
+        group_reduction_axes = list(range(1, len(group_shape)))
+        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
+        if not is_instance_norm:
+            axis = -2 if self.axis == -1 else self.axis - 1
+        else:
+            axis = -1 if self.axis == -1 else self.axis - 1
+        group_reduction_axes.pop(axis)
+
+        mean, variance = tf.nn.moments(reshaped_inputs, group_reduction_axes, keepdims=True)
+
+        gamma, beta = self._get_reshaped_weights(input_shape)
+        normalized_inputs = tf.nn.batch_normalization(
+            reshaped_inputs,
+            mean=mean,
+            variance=variance,
+            scale=gamma,
+            offset=beta,
+            variance_epsilon=self.epsilon,
+        )
+        return normalized_inputs
+
+    def _get_reshaped_weights(self, input_shape):
+        broadcast_shape = self._create_broadcast_shape(input_shape)
+        gamma = None
+        beta = None
+        if self.scale:
+            gamma = tf.reshape(self.gamma, broadcast_shape)
+
+        if self.center:
+            beta = tf.reshape(self.beta, broadcast_shape)
+        return gamma, beta
+
+    def _check_if_input_shape_is_none(self, input_shape):
+        dim = input_shape[self.axis]
+        if dim is None:
+            raise ValueError(
+                "Axis " + str(self.axis) + " of "
+                "input tensor should have a defined dimension "
+                "but the layer received an input with shape " + str(input_shape) + "."
+            )
+
+    def _set_number_of_groups_for_instance_norm(self, input_shape):
+        dim = input_shape[self.axis]
+
+        if self.groups == -1:
+            self.groups = dim
+
+    def _check_size_of_dimensions(self, input_shape):
+
+        dim = input_shape[self.axis]
+        if dim < self.groups:
+            raise ValueError(
+                "Number of groups (" + str(self.groups) + ") cannot be "
+                "more than the number of channels (" + str(dim) + ")."
+            )
+
+        if dim % self.groups != 0:
+            raise ValueError(
+                "Number of groups (" + str(self.groups) + ") must be a "
+                "multiple of the number of channels (" + str(dim) + ")."
+            )
+
+    def _check_axis(self):
+
+        if self.axis == 0:
+            raise ValueError(
+                "You are trying to normalize your batch axis. Do you want to "
+                "use tf.layer.batch_normalization instead"
+            )
+
+    def _create_input_spec(self, input_shape):
+
+        dim = input_shape[self.axis]
+        self.input_spec = tf.keras.layers.InputSpec(ndim=len(input_shape), axes={self.axis: dim})
+
+    def _add_gamma_weight(self, input_shape):
+
+        dim = input_shape[self.axis]
+        shape = (dim,)
+
+        if self.scale:
+            self.gamma = self.add_weight(
+                shape=shape,
+                name="gamma",
+                initializer=self.gamma_initializer,
+                regularizer=self.gamma_regularizer,
+                constraint=self.gamma_constraint,
+            )
+        else:
+            self.gamma = None
+
+    def _add_beta_weight(self, input_shape):
+
+        dim = input_shape[self.axis]
+        shape = (dim,)
+
+        if self.center:
+            self.beta = self.add_weight(
+                shape=shape,
+                name="beta",
+                initializer=self.beta_initializer,
+                regularizer=self.beta_regularizer,
+                constraint=self.beta_constraint,
+            )
+        else:
+            self.beta = None
+
+    def _create_broadcast_shape(self, input_shape):
+        broadcast_shape = [1] * len(input_shape)
+        is_instance_norm = (input_shape[self.axis] // self.groups) == 1
+        if not is_instance_norm:
+            broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
+            broadcast_shape.insert(self.axis, self.groups)
+        else:
+            broadcast_shape[self.axis] = self.groups
+        return broadcast_shape
+
+
+class TFWav2Vec2WeightNormConv1D(tf.keras.layers.Conv1D):
+    """Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm"""
+
+    def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
+        super().__init__(
+            filters=filters,
+            kernel_size=kernel_size,
+            groups=groups,
+            padding="valid",
+            use_bias=True,
+            bias_initializer="he_normal",
+            **kwargs,
+        )
+        self.explicit_padding = explicit_padding
+        self.filter_axis = 2
+        self.initialized = False
+        self.kernel_norm_axes = tf.constant([0, 1])
+
+    def _init_norm(self):
+        """Set the norm of the weight vector."""
+        kernel_norm = tf.sqrt(tf.reduce_sum(tf.square(self.weight_v), axis=self.kernel_norm_axes))
+        self.weight_g.assign(kernel_norm[:, tf.newaxis, tf.newaxis])
+
+    def _normalize_kernel(self):
+        """Generate normalized weights."""
+        kernel = tf.nn.l2_normalize(self.weight_v, axis=self.kernel_norm_axes) * tf.transpose(self.weight_g)
+        self.kernel = tf.transpose(kernel)
+
+    def build(self, input_shape):
+        if not self.built:
+            super().build(input_shape)
+            self.kernel = tf.Variable(tf.transpose(self.kernel), name="weight_v", trainable=True)
+            self.weight_v = self.kernel
+
+            self.weight_g = self.add_weight(
+                name="weight_g",
+                shape=(int(self.weight_v.shape[self.filter_axis]), 1, 1),
+                initializer="ones",
+                dtype=self.weight_v.dtype,
+                trainable=True,
+            )
+            self.bias = self.add_weight(name="bias", shape=(self.filters,), initializer="zeros", trainable=True)
+
+    def call(self, inputs):
+        if not self.initialized:
+            self._init_norm()
+            self.initialized = True
+
+        self._normalize_kernel()
+
+        padded_inputs = tf.pad(inputs, ((0, 0), (self.explicit_padding, self.explicit_padding), (0, 0)))
+        output = super().call(padded_inputs)
+
+        return output
+
+
+class TFWav2Vec2NoLayerNormConvLayer(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
+        self.out_conv_dim = config.conv_dim[layer_id]
+
+        self.conv = tf.keras.layers.Conv1D(
+            filters=self.out_conv_dim,
+            kernel_size=config.conv_kernel[layer_id],
+            strides=config.conv_stride[layer_id],
+            use_bias=config.conv_bias,
+            name="conv",
+        )
+        self.activation = get_tf_activation(config.feat_extract_activation)
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.conv(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+class TFWav2Vec2LayerNormConvLayer(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
+        self.out_conv_dim = config.conv_dim[layer_id]
+
+        self.conv = tf.keras.layers.Conv1D(
+            filters=self.out_conv_dim,
+            kernel_size=config.conv_kernel[layer_id],
+            strides=config.conv_stride[layer_id],
+            use_bias=config.conv_bias,
+            name="conv",
+        )
+        self.layer_norm = tf.keras.layers.LayerNormalization(name="layer_norm", epsilon=config.layer_norm_eps)
+        self.activation = get_tf_activation(config.feat_extract_activation)
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.conv(hidden_states)
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+class TFWav2Vec2GroupNormConvLayer(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
+        self.out_conv_dim = config.conv_dim[layer_id]
+
+        self.conv = tf.keras.layers.Conv1D(
+            filters=self.out_conv_dim,
+            kernel_size=config.conv_kernel[layer_id],
+            strides=config.conv_stride[layer_id],
+            use_bias=config.conv_bias,
+            name="conv",
+        )
+        self.activation = get_tf_activation(config.feat_extract_activation)
+        self.layer_norm = TFWav2Vec2GroupNorm(
+            groups=self.out_conv_dim, epsilon=config.layer_norm_eps, name="layer_norm"
+        )
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.conv(hidden_states)
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+class TFWav2Vec2PositionalConvEmbedding(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.conv = TFWav2Vec2WeightNormConv1D(
+            filters=config.hidden_size,
+            kernel_size=config.num_conv_pos_embeddings,
+            groups=config.num_conv_pos_embedding_groups,
+            explicit_padding=config.num_conv_pos_embeddings // 2,
+            name="conv",
+        )
+        self.padding = TFWav2Vec2SamePadLayer(config.num_conv_pos_embeddings)
+        self.activation = get_tf_activation(config.feat_extract_activation)
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.conv(hidden_states)
+        hidden_states = self.padding(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        return hidden_states
+
+
+class TFWav2Vec2SamePadLayer(tf.keras.layers.Layer):
+    def __init__(self, num_conv_pos_embeddings, **kwargs):
+        super().__init__(**kwargs)
+        self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
+
+    def call(self, hidden_states):
+        if self.num_pad_remove > 0:
+            hidden_states = hidden_states[:, : -self.num_pad_remove, :]
+        return hidden_states
+
+
+class TFWav2Vec2FeatureExtractor(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+
+        if config.feat_extract_norm == "group":
+            conv_layers = [TFWav2Vec2GroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [
+                TFWav2Vec2NoLayerNormConvLayer(config, layer_id=i + 1, name=f"conv_layers.{i+1}")
+                for i in range(config.num_feat_extract_layers - 1)
+            ]
+        elif config.feat_extract_norm == "layer":
+            conv_layers = [
+                TFWav2Vec2LayerNormConvLayer(config, layer_id=i, name=f"conv_layers.{i}")
+                for i in range(config.num_feat_extract_layers)
+            ]
+        else:
+            raise ValueError(
+                f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
+            )
+        self.conv_layers = conv_layers
+
+    def call(self, input_values):
+        hidden_states = tf.expand_dims(input_values, -1)
+        for conv_layer in self.conv_layers:
+            hidden_states = conv_layer(hidden_states)
+        return hidden_states
+
+
+class TFWav2Vec2FeatureProjection(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.projection = tf.keras.layers.Dense(
+            units=config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="projection",
+        )
+        self.dropout = tf.keras.layers.Dropout(rate=config.feat_proj_dropout)
+
+    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states = self.projection(hidden_states)
+        hidden_states = self.dropout(hidden_states, training=training)
+        return hidden_states
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFWav2Vec2
+class TFWav2Vec2Attention(tf.keras.layers.Layer):
+    """Multi-headed attention from "Attention Is All You Need"""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embed_dim = embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = tf.keras.layers.Dropout(dropout)
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+        self.is_decoder = is_decoder
+
+        self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
+        self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
+        self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
+        self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+
+    def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+        return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        key_value_states: Optional[tf.Tensor] = None,
+        past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None,
+        attention_mask: Optional[tf.Tensor] = None,
+        layer_head_mask: Optional[tf.Tensor] = None,
+        training=False,
+    ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+        bsz, tgt_len, embed_dim = shape_list(hidden_states)
+
+        # get query proj
+        query_states = self.q_proj(hidden_states) * self.scaling
+        # get key, value proj
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_states = past_key_value[0]
+            value_states = past_key_value[1]
+        elif is_cross_attention:
+            # cross_attentions
+            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+        elif past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+            key_states = tf.concat([past_key_value[0], key_states], axis=2)
+            value_states = tf.concat([past_key_value[1], value_states], axis=2)
+        else:
+            # self_attention
+            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_states, value_states)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
+        key_states = tf.reshape(key_states, proj_shape)
+        value_states = tf.reshape(value_states, proj_shape)
+
+        src_len = shape_list(key_states)[1]
+        attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
+
+        # The tf.debugging asserts are not compliant with XLA then they
+        # have to be disabled in other modes than eager.
+        if tf.executing_eagerly():
+            tf.debugging.assert_equal(
+                shape_list(attn_weights),
+                [bsz * self.num_heads, tgt_len, src_len],
+                message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}",
+            )
+
+        if attention_mask is not None:
+            # The tf.debugging asserts are not compliant with XLA then they
+            # have to be disabled in other modes than eager.
+            if tf.executing_eagerly():
+                tf.debugging.assert_equal(
+                    shape_list(attention_mask),
+                    [bsz, 1, tgt_len, src_len],
+                    message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}",
+                )
+
+            attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
+            attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
+            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+        attn_weights = tf.nn.softmax(attn_weights, axis=-1)
+
+        if layer_head_mask is not None:
+            # The tf.debugging asserts are not compliant with XLA then they
+            # have to be disabled in other modes than eager.
+            if tf.executing_eagerly():
+                tf.debugging.assert_equal(
+                    shape_list(layer_head_mask),
+                    [self.num_heads],
+                    message=f"Head mask for a single layer should be of size {(self.num_heads)}, but is {shape_list(layer_head_mask)}",
+                )
+
+            attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+                attn_weights, (bsz, self.num_heads, tgt_len, src_len)
+            )
+            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+        attn_probs = self.dropout(attn_weights, training=training)
+        attn_output = tf.matmul(attn_probs, value_states)
+
+        # The tf.debugging asserts are not compliant with XLA then they
+        # have to be disabled in other modes than eager.
+        if tf.executing_eagerly():
+            tf.debugging.assert_equal(
+                shape_list(attn_output),
+                [bsz * self.num_heads, tgt_len, self.head_dim],
+                message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}",
+            )
+
+        attn_output = tf.transpose(
+            tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
+        )
+        attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
+
+        attn_output = self.out_proj(attn_output)
+        attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
+
+        return attn_output, attn_weights, past_key_value
+
+
+class TFWav2Vec2FeedForward(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+
+        self.intermediate_dropout = tf.keras.layers.Dropout(config.activation_dropout)
+
+        self.intermediate_dense = tf.keras.layers.Dense(
+            units=config.intermediate_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="intermediate_dense",
+        )
+        self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+
+        self.output_dense = tf.keras.layers.Dense(
+            units=config.hidden_size,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="output_dense",
+        )
+        self.output_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+
+    def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+        hidden_states = self.intermediate_dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        hidden_states = self.intermediate_dropout(hidden_states, training=training)
+
+        hidden_states = self.output_dense(hidden_states)
+        hidden_states = self.output_dropout(hidden_states, training=training)
+        return hidden_states
+
+
+class TFWav2Vec2EncoderLayer(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+        self.attention = TFWav2Vec2Attention(
+            embed_dim=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            dropout=config.attention_dropout,
+            is_decoder=False,
+            name="attention",
+        )
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward")
+        self.final_layer_norm = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="final_layer_norm"
+        )
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        attn_residual = hidden_states
+        hidden_states, attn_weights, _ = self.attention(
+            hidden_states, attention_mask=attention_mask, training=training
+        )
+        hidden_states = self.dropout(hidden_states, training=training)
+        hidden_states = attn_residual + hidden_states
+
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states = hidden_states + self.feed_forward(hidden_states)
+        hidden_states = self.final_layer_norm(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class TFWav2Vec2EncoderLayerStableLayerNorm(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+        self.attention = TFWav2Vec2Attention(
+            embed_dim=config.hidden_size,
+            num_heads=config.num_attention_heads,
+            dropout=config.attention_dropout,
+            is_decoder=False,
+            name="attention",
+        )
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward")
+        self.final_layer_norm = tf.keras.layers.LayerNormalization(
+            epsilon=config.layer_norm_eps, name="final_layer_norm"
+        )
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        training: bool = False,
+    ) -> Tuple[tf.Tensor]:
+        attn_residual = hidden_states
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states, attn_weights, _ = self.attention(
+            hidden_states, attention_mask=attention_mask, training=training
+        )
+        hidden_states = self.dropout(hidden_states, training=training)
+        hidden_states = attn_residual + hidden_states
+        hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class TFWav2Vec2Encoder(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
+        self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.layer = [TFWav2Vec2EncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        training: Optional[bool] = False,
+    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        if attention_mask is not None:
+            hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
+            attention_mask = _expand_mask(attention_mask)
+        else:
+            attention_mask = None
+
+        position_embeddings = self.pos_conv_embed(hidden_states)
+        hidden_states = hidden_states + position_embeddings
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states = self.dropout(hidden_states, training=training)
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+            dropout_probability = np.random.uniform(0, 1)
+            if training and (dropout_probability < self.config.layerdrop):  # skip the layer
+                continue
+
+            layer_outputs = layer_module(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                training=training,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        # Add last layer
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class TFWav2Vec2EncoderStableLayerNorm(tf.keras.layers.Layer):
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
+        self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.layer = [
+            TFWav2Vec2EncoderLayerStableLayerNorm(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
+        ]
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        training: Optional[bool] = False,
+    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        if attention_mask is not None:
+            hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
+            attention_mask = _expand_mask(attention_mask)
+        else:
+            attention_mask = None
+
+        position_embeddings = self.pos_conv_embed(hidden_states)
+        hidden_states = hidden_states + position_embeddings
+        hidden_states = self.dropout(hidden_states, training=training)
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+            dropout_probability = np.random.uniform(0, 1)
+            if training and (dropout_probability < self.config.layerdrop):  # skip the layer
+                continue
+
+            layer_outputs = layer_module(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                training=training,
+            )
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+@keras_serializable
+class TFWav2Vec2MainLayer(tf.keras.layers.Layer):
+    config_class = Wav2Vec2Config
+
+    def __init__(self, config: Wav2Vec2Config, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
+        self.feature_extractor = TFWav2Vec2FeatureExtractor(config, name="feature_extractor")
+        self.feature_projection = TFWav2Vec2FeatureProjection(config, name="feature_projection")
+
+        if config.do_stable_layer_norm:
+            self.encoder = TFWav2Vec2EncoderStableLayerNorm(config, name="encoder")
+        else:
+            self.encoder = TFWav2Vec2Encoder(config, name="encoder")
+
+    def build(self, input_shape: tf.TensorShape):
+        self.masked_spec_embed = self.add_weight(
+            shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed"
+        )
+
+        super().build(input_shape)
+
+    def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
+        """
+        Computes the output length of the convolutional layers
+        """
+
+        def _conv_out_length(input_length, kernel_size, stride):
+            # 1D convolutional layer output length formula taken
+            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
+            return (input_length - kernel_size) // stride + 1
+
+        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
+            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
+
+        return input_lengths
+
+    def _mask_hidden_states(
+        self, hidden_states: tf.Tensor, mask_time_indices: Optional[tf.Tensor] = None, training: bool = False
+    ):
+        """
+        Masks extracted features along time axis and/or along feature axis according to `SpecAugment
+        <https://arxiv.org/abs/1904.08779>`__ .
+        """
+
+        # `config.apply_spec_augment` can set masking to False
+        if not getattr(self.config, "apply_spec_augment", True):
+            return hidden_states
+
+        if mask_time_indices is not None:
+            # apply SpecAugment along time axis with given mask_time_indices
+            hidden_states = tf.tensor_scatter_nd_update(hidden_states, mask_time_indices, self.masked_spec_embed)
+        elif self.config.mask_time_prob > 0 and training:
+            # generate indices & apply SpecAugment along time axis
+            batch_size, sequence_length, hidden_size = hidden_states.shape
+
+            mask_time_indices = _compute_mask_indices(
+                (batch_size, sequence_length),
+                self.config.mask_time_prob,
+                self.config.mask_time_length,
+                min_masks=2,
+            )
+            hidden_states = tf.tensor_scatter_nd_update(hidden_states, mask_time_indices, self.masked_spec_embed)
+
+        # apply SpecAugment along feature axis
+        if self.config.mask_feature_prob > 0 and training:
+            mask_feature_indices = _compute_mask_indices(
+                (batch_size, hidden_size),
+                mask_prob=self.config.mask_feature_prob,
+                mask_length=self.config.mask_feature_length,
+            )
+            hidden_states = tf.tensor_scatter_nd_update(hidden_states, mask_feature_indices, self.masked_spec_embed)
+
+        return hidden_states
+
+    def call(
+        self,
+        input_values: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        output_attentions: Optional[tf.Tensor] = None,
+        output_hidden_states: Optional[tf.Tensor] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs: Any,
+    ):
+        inputs = input_values_processing(
+            func=self.call,
+            config=self.config,
+            input_values=input_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        hidden_states = self.feature_extractor(
+            tf.cast(inputs["input_values"], tf.float32), training=inputs["training"]
+        )
+
+        if inputs["attention_mask"] is not None:
+            # compute real output lengths according to convolution formula
+            output_lengths = self._get_feat_extract_output_lengths(tf.reduce_sum(inputs["attention_mask"], -1))
+            attention_mask = tf.sequence_mask(output_lengths, dtype=hidden_states.dtype)
+
+        hidden_states = self.feature_projection(hidden_states, training=inputs["training"])
+
+        mask_time_indices = kwargs.get("mask_time_indices", None)
+        if mask_time_indices is not None:  # apply SpecAugment along time axis with given indices
+            hidden_states = tf.tensor_scatter_nd_update(hidden_states, mask_time_indices, self.mask_spec_embed)
+
+        hidden_states = self._mask_hidden_states(hidden_states)
+
+        encoder_outputs = self.encoder(
+            hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        hidden_states = encoder_outputs[0]
+
+        if not inputs["return_dict"]:
+            return (hidden_states,) + encoder_outputs[1:]
+
+        return TFBaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = Wav2Vec2Config
+    base_model_prefix = "wav2vec2"
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        pad_token = 0.0
+        input_values = tf.convert_to_tensor(np.random.rand(1, 16000), tf.float32)
+        dummy_inputs = {
+            "input_values": input_values,
+            "attention_mask": tf.cast(tf.not_equal(input_values, pad_token), tf.float32),
+        }
+        return dummy_inputs
+
+    @tf.function
+    def serving(self, inputs):
+        output = self.call(input_values=inputs, training=False)
+
+        return self.serving_output(output)
+
+
+WAV_2_VEC_2_START_DOCSTRING = r"""
+
+    This model inherits from :class:`~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.
+
+    .. note::
+
+        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 :meth:`tf.keras.Model.fit` method which currently requires having all
+        the tensors in the first argument of the model call function: :obj:`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 :obj:`input_values` only and nothing else: :obj:`model(inputs_ids)`
+        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+          :obj:`model([input_values, attention_mask])` or :obj:`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:
+          :obj:`model({"input_values": input_values, "token_type_ids": token_type_ids})`
+
+    Args:
+        config (:class:`~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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_values (:obj:`np.ndarray`, :obj:`tf.Tensor`, :obj:`List[tf.Tensor]` :obj:`Dict[str, tf.Tensor]` or :obj:`Dict[str, np.ndarray]` and each example must have the shape :obj:`({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using :class:`~transformers.BertTokenizer`. See
+            :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
+            details.
+
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({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.html#attention-mask>`__
+        token_type_ids (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({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.html#token-type-ids>`__
+        position_ids (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({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.html#position-ids>`__
+        head_mask (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(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 (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
+            Optionally, instead of passing :obj:`input_values` you can choose to directly pass an embedded
+            representation. This is useful if you want more control over how to convert :obj:`input_values` indices
+            into associated vectors than the model's internal embedding lookup matrix.
+        output_attentions (:obj:`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 (:obj:`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 (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~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 (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+    "The bare TFWav2Vec2 Model transformer outputing raw hidden-states without any specific head on top.",
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
+    def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.config = config
+        self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_values: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs: Any,
+    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
+        """
+
+        Returns:
+
+        Example::
+
+            >>> 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("patrickvonplaten/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
+        """
+
+        inputs = input_values_processing(
+            func=self.call,
+            config=self.config,
+            input_values=input_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        inputs["output_hidden_states"] = (
+            inputs["output_hidden_states"] if inputs["output_hidden_states"] else self.config.output_hidden_states
+        )
+        inputs["output_attentions"] = (
+            inputs["output_attentions"] if inputs["output_attentions"] else self.config.output_attentions
+        )
+        inputs["return_dict"] = inputs["return_dict"] if inputs["return_dict"] else self.config.return_dict
+
+        outputs = self.wav2vec2(
+            input_values=inputs["input_values"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
+    def serving_output(self, output):
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+        return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
+
+
+@add_start_docstrings(
+    """TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). """,
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
+    def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
+        self.dropout = tf.keras.layers.Dropout(config.final_dropout)
+        self.lm_head = tf.keras.layers.Dense(config.vocab_size, name="lm_head")
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameter
+        will not be updated during training.
+        """
+        self.wav2vec2.feature_extractor.trainable = False
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFCausalLMOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        input_values: tf.Tensor,
+        attention_mask: Optional[tf.Tensor] = None,
+        token_type_ids: Optional[tf.Tensor] = None,
+        position_ids: Optional[tf.Tensor] = None,
+        head_mask: Optional[tf.Tensor] = None,
+        inputs_embeds: Optional[tf.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        labels: Optional[tf.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+        **kwargs: Any,
+    ) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(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]``
+
+        Returns:
+
+        Example::
+
+            >>> 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("patrickvonplaten/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_values
+
+            >>> loss = model(input_values, labels=labels).loss
+        """
+        inputs = input_values_processing(
+            func=self.call,
+            config=self.config,
+            input_values=input_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.wav2vec2(
+            input_values=inputs["input_values"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states, training=inputs["training"])
+
+        logits = self.lm_head(hidden_states)
+
+        if labels is not None:
+            attention_mask = (
+                inputs["attention_mask"]
+                if inputs["attention_mask"] is not None
+                else tf.ones_like(inputs["input_values"], dtype=tf.float32)
+            )
+            input_lengths = self.wav2vec2._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, axis=-1))
+
+            # assuming that padded tokens are filled with -100
+            # when not being attended to
+            labels_mask = tf.cast(labels >= 0, tf.int32)
+            target_lengths = tf.reduce_sum(labels_mask, axis=-1)
+            flattened_labels = tf.boolean_mask(labels, labels_mask)
+            flattened_labels = tf.reshape(flattened_labels, [labels.shape[0], -1])
+
+            loss = tf.nn.ctc_loss(
+                logits=logits,
+                labels=flattened_labels,
+                logit_length=input_lengths,
+                label_length=target_lengths,
+                blank_index=self.config.pad_token_id,
+                logits_time_major=False,
+            )
+
+            if self.config.ctc_loss_reduction == "sum":
+                loss = tf.reduce_sum(loss)
+            if self.config.ctc_loss_reduction == "mean":
+                loss = tf.reduce_mean(loss)
+        else:
+            loss = None
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFCausalLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
+        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+        return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -510,14 +510,6 @@ class Wav2Vec2FeedForward(nn.Module):
         return hidden_states
 
 
-class Wav2Vec2Output(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-    def forward(self, hidden_states, input_tensor):
-        return hidden_states
-
-
 class Wav2Vec2EncoderLayer(nn.Module):
     def __init__(self, config):
         super().__init__()

src/transformers/utils/dummy_tf_objects.py

@@ -1647,6 +1647,32 @@ class TFTransfoXLPreTrainedModel:
         requires_backends(cls, ["tf"])
 
 
+TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFWav2Vec2ForCTC:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFWav2Vec2Model:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFWav2Vec2PreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
 TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/test_modeling_tf_common.py

@@ -445,6 +445,8 @@ class TFModelTesterMixin:
             for name, key in self._prepare_for_class(inputs_dict, model_class).items():
                 if type(key) == bool:
                     pt_inputs_dict[name] = key
+                elif name == "input_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                 else:
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
 
@@ -455,6 +457,7 @@ class TFModelTesterMixin:
             with torch.no_grad():
                 pto = pt_model(**pt_inputs_dict)
             tfo = tf_model(self._prepare_for_class(inputs_dict, model_class), training=False)
+
             tf_hidden_states = tfo[0].numpy()
             pt_hidden_states = pto[0].numpy()
 
@@ -486,6 +489,8 @@ class TFModelTesterMixin:
                 if type(key) == bool:
                     key = np.array(key, dtype=bool)
                     pt_inputs_dict[name] = torch.from_numpy(key).to(torch.long)
+                elif name == "input_values":
+                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                 else:
                     pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)
             # need to rename encoder-decoder "inputs" for PyTorch
@@ -1061,7 +1066,7 @@ class TFModelTesterMixin:
 
     def test_lm_head_model_random_no_beam_search_generate(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        input_ids = inputs_dict["input_ids"]
+        input_ids = inputs_dict.get("input_ids", None)
 
         # iterate over all generative models
         for model_class in self.all_generative_model_classes:
@@ -1097,7 +1102,7 @@ class TFModelTesterMixin:
 
     def test_lm_head_model_random_beam_search_generate(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        input_ids = inputs_dict["input_ids"]
+        input_ids = inputs_dict.get("input_ids", None)
 
         for model_class in self.all_generative_model_classes:
             model = model_class(config)

tests/test_modeling_tf_wav2vec2.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import copy
+import inspect
+import math
+import unittest
+
+import numpy as np
+
+from transformers import Wav2Vec2Config, is_tf_available
+from transformers.testing_utils import require_datasets, require_soundfile, require_tf, slow
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFWav2Vec2ForCTC, TFWav2Vec2Model, Wav2Vec2Processor
+    from transformers.models.wav2vec2.modeling_tf_wav2vec2 import _compute_mask_indices
+
+
+@require_tf
+class TFWav2Vec2ModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=1024,
+        is_training=False,
+        hidden_size=16,
+        feat_extract_norm="group",
+        feat_extract_dropout=0.0,
+        feat_extract_activation="gelu",
+        conv_dim=(32, 32, 32),
+        conv_stride=(4, 4, 4),
+        conv_kernel=(8, 8, 8),
+        conv_bias=False,
+        num_conv_pos_embeddings=16,
+        num_conv_pos_embedding_groups=2,
+        num_hidden_layers=4,
+        num_attention_heads=2,
+        hidden_dropout_prob=0.1,  # this is most likely not correctly set yet
+        intermediate_size=20,
+        layer_norm_eps=1e-5,
+        hidden_act="gelu",
+        initializer_range=0.02,
+        vocab_size=32,
+        do_stable_layer_norm=False,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.feat_extract_norm = feat_extract_norm
+        self.feat_extract_dropout = feat_extract_dropout
+        self.feat_extract_activation = feat_extract_activation
+        self.conv_dim = conv_dim
+        self.conv_stride = conv_stride
+        self.conv_kernel = conv_kernel
+        self.conv_bias = conv_bias
+        self.num_conv_pos_embeddings = num_conv_pos_embeddings
+        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.intermediate_size = intermediate_size
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.vocab_size = vocab_size
+        self.do_stable_layer_norm = do_stable_layer_norm
+        self.scope = scope
+
+        output_seq_length = self.seq_length
+        for kernel, stride in zip(self.conv_kernel, self.conv_stride):
+            output_seq_length = (output_seq_length - (kernel - 1)) / stride
+        self.output_seq_length = int(math.ceil(output_seq_length))
+        self.encoder_seq_length = self.output_seq_length
+
+    def prepare_config_and_inputs(self):
+        input_values = tf.cast(ids_tensor([self.batch_size, self.seq_length], 32768), tf.float32) / 32768.0
+        attention_mask = tf.ones_like(input_values)
+
+        config = Wav2Vec2Config(
+            hidden_size=self.hidden_size,
+            feat_extract_norm=self.feat_extract_norm,
+            feat_extract_dropout=self.feat_extract_dropout,
+            feat_extract_activation=self.feat_extract_activation,
+            conv_dim=self.conv_dim,
+            conv_stride=self.conv_stride,
+            conv_kernel=self.conv_kernel,
+            conv_bias=self.conv_bias,
+            num_conv_pos_embeddings=self.num_conv_pos_embeddings,
+            num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            intermediate_size=self.intermediate_size,
+            layer_norm_eps=self.layer_norm_eps,
+            hidden_act=self.hidden_act,
+            initializer_range=self.initializer_range,
+            vocab_size=self.vocab_size,
+            do_stable_layer_norm=self.do_stable_layer_norm,
+        )
+
+        return config, input_values, attention_mask
+
+    def create_and_check_model(self, config, input_values, attention_mask):
+        model = TFWav2Vec2Model(config)
+        result = model(input_values, attention_mask=attention_mask)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
+        )
+
+    def create_and_check_batch_inference(self, config, input_values, *args):
+        # test does not pass for models making use of `group_norm`
+        # check: https://github.com/pytorch/fairseq/issues/3227
+        config.layerdrop = 0.0
+        model = TFWav2Vec2Model(config)
+
+        input_values = input_values[:3]
+        attention_mask = tf.ones_like(input_values)
+
+        input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
+        length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
+
+        # convert values that are over input_lengths to padding
+        input_values = input_values * length_mask
+        attention_mask = attention_mask * length_mask
+
+        batch_outputs = model(input_values, attention_mask=attention_mask, training=False).last_hidden_state
+
+        for i in range(input_values.shape[0]):
+            input_slice = input_values[i : i + 1, : input_lengths[i]]
+            output = model(input_slice, training=False).last_hidden_state
+
+            batch_output = batch_outputs[i : i + 1, : output.shape[1]]
+            self.parent.assertTrue(np.allclose(output, batch_output, atol=1e-3))
+
+    def check_ctc_loss(self, config, input_values, *args):
+        model = TFWav2Vec2ForCTC(config)
+
+        input_values = input_values[:3]
+        attention_mask = tf.ones_like(input_values)
+
+        input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
+        max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
+        labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
+
+        length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
+
+        # convert values that are over input_lengths to padding
+        input_values = input_values * length_mask
+        attention_mask = attention_mask * length_mask
+
+        model.config.ctc_loss_reduction = "sum"
+        sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
+
+        model.config.ctc_loss_reduction = "mean"
+        mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
+
+        self.parent.assertTrue(abs(labels.shape[0] * mean_loss - sum_loss) < 1e-2)
+
+    def check_training(self, config, input_values, *args):
+        model = TFWav2Vec2ForCTC(config)
+
+        # freeze feature encoder
+        model.freeze_feature_extractor()
+
+        input_values = input_values[:3]
+
+        input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
+        max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
+        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
+
+        length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
+
+        input_values = input_values * length_mask
+
+        pad_size = max(max_length_labels) - labels.shape[1]
+        labels = tf.pad(labels, ((0, 0), (0, pad_size)), constant_values=-100)
+
+        loss = model(input_values, labels=labels, training=True).loss
+
+        self.parent.assertFalse(tf.math.is_inf(loss))
+
+    def prepare_config_and_inputs_for_common(self):
+        config, input_values, attention_mask = self.prepare_config_and_inputs()
+        inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class TFWav2Vec2ModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (TFWav2Vec2Model, TFWav2Vec2ForCTC) if is_tf_available() else ()
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFWav2Vec2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # overwrite because input_values != input_ids
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["input_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    # overwrite because input_values != input_ids
+    def test_keyword_and_dict_args(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            outputs_dict = model(inputs)
+
+            inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
+            input_values = inputs_keywords.pop("input_values", None)
+            outputs_keywords = model(input_values, **inputs_keywords)
+            output_dict = outputs_dict[0].numpy()
+            output_keywords = outputs_keywords[0].numpy()
+
+            self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_hidden_states_output(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def check_hidden_states_output(config, inputs_dict, model_class):
+            model = model_class(config)
+            outputs = model(self._prepare_for_class(inputs_dict, model_class))
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+
+            hidden_states = outputs.hidden_states
+            self.assertEqual(config.output_attentions, False)
+            self.assertEqual(len(hidden_states), expected_num_layers)
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [self.model_tester.output_seq_length, self.model_tester.hidden_size],
+            )
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(config, inputs_dict, model_class)
+
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            check_hidden_states_output(config, inputs_dict, model_class)
+
+    def test_ctc_loss_inference(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_ctc_loss(*config_and_inputs)
+
+    def test_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_training(*config_and_inputs)
+
+    # Wav2Vec2 has no inputs_embeds
+    def test_inputs_embeds(self):
+        pass
+
+    # Wav2Vec2 cannot resize token embeddings
+    # since it has no tokens embeddings
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    # Wav2Vec2 has no inputs_embeds
+    # and thus the `get_input_embeddings` fn
+    # is not implemented
+    def test_model_common_attributes(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
+        self.assertIsNotNone(model)
+
+
+@require_tf
+class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFWav2Vec2Model, TFWav2Vec2ForCTC) if is_tf_available() else ()
+    test_resize_embeddings = False
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFWav2Vec2ModelTester(
+            self,
+            conv_stride=(3, 3, 3),
+            feat_extract_norm="layer",
+            do_stable_layer_norm=True,
+            scope="robust",
+        )
+        self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
+
+    # overwrite because input_values != input_ids
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["input_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    # overwrite because input_values != input_ids
+    def test_keyword_and_dict_args(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            outputs_dict = model(inputs)
+
+            inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
+            input_values = inputs_keywords.pop("input_values", None)
+            outputs_keywords = model(input_values, **inputs_keywords)
+            output_dict = outputs_dict[0].numpy()
+            output_keywords = outputs_keywords[0].numpy()
+
+            self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_hidden_states_output(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def check_hidden_states_output(config, inputs_dict, model_class):
+            model = model_class(config)
+            outputs = model(self._prepare_for_class(inputs_dict, model_class))
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+
+            hidden_states = outputs.hidden_states
+            self.assertEqual(config.output_attentions, False)
+            self.assertEqual(len(hidden_states), expected_num_layers)
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [self.model_tester.output_seq_length, self.model_tester.hidden_size],
+            )
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(config, inputs_dict, model_class)
+
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            check_hidden_states_output(config, inputs_dict, model_class)
+
+    def test_batched_inference(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_batch_inference(*config_and_inputs)
+
+    def test_ctc_loss_inference(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_ctc_loss(*config_and_inputs)
+
+    def test_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_training(*config_and_inputs)
+
+    # Wav2Vec2 has no inputs_embeds
+    def test_inputs_embeds(self):
+        pass
+
+    # Wav2Vec2 cannot resize token embeddings
+    # since it has no tokens embeddings
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    # Wav2Vec2 has no inputs_embeds
+    # and thus the `get_input_embeddings` fn
+    # is not implemented
+    def test_model_common_attributes(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
+        self.assertIsNotNone(model)
+
+
+@require_tf
+class TFWav2Vec2UtilsTest(unittest.TestCase):
+    def test_compute_mask_indices(self):
+        batch_size = 4
+        sequence_length = 60
+        mask_prob = 0.5
+        mask_length = 1
+
+        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
+
+        self.assertListEqual(
+            tf.reduce_sum(mask, -1).numpy().tolist(), [mask_prob * sequence_length for _ in range(batch_size)]
+        )
+
+    def test_compute_mask_indices_overlap(self):
+        batch_size = 4
+        sequence_length = 80
+        mask_prob = 0.5
+        mask_length = 4
+
+        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
+
+        # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
+        for batch_sum in tf.reduce_sum(mask, -1):
+            self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
+
+
+@require_tf
+@slow
+@require_datasets
+@require_soundfile
+class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
+    def _load_datasamples(self, num_samples):
+        from datasets import load_dataset
+
+        import soundfile as sf
+
+        ids = [f"1272-141231-000{i}" for i in range(num_samples)]
+
+        # map files to raw
+        def map_to_array(batch):
+            speech, _ = sf.read(batch["file"])
+            batch["speech"] = speech
+            return batch
+
+        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+
+        ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
+
+        return ds["speech"][:num_samples]
+
+    def test_inference_ctc_normal(self):
+        model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
+        input_speech = self._load_datasamples(1)
+
+        input_values = processor(input_speech, return_tensors="tf", sampling_rate=16000).input_values
+
+        logits = model(input_values).logits
+
+        predicted_ids = tf.argmax(logits, axis=-1)
+        predicted_trans = processor.batch_decode(predicted_ids)
+
+        EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
+        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
+
+    def test_inference_ctc_normal_batched(self):
+        model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+        processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
+
+        input_speech = self._load_datasamples(2)
+
+        input_values = processor(
+            input_speech, return_tensors="tf", padding=True, truncation=True, sampling_rate=16000
+        ).input_values
+
+        logits = model(input_values).logits
+
+        predicted_ids = tf.argmax(logits, axis=-1)
+        predicted_trans = processor.batch_decode(predicted_ids)
+
+        EXPECTED_TRANSCRIPTIONS = [
+            "a man said to the universe sir i exist",
+            "sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore",
+        ]
+        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
+
+    def test_inference_ctc_robust_batched(self):
+        model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
+        processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
+
+        input_speech = self._load_datasamples(4)
+
+        inputs = processor(input_speech, return_tensors="tf", padding=True, truncation=True)
+
+        input_values = inputs.input_values
+        attention_mask = inputs.attention_mask
+
+        logits = model(input_values, attention_mask=attention_mask).logits
+
+        predicted_ids = tf.argmax(logits, dim=-1)
+        predicted_trans = processor.batch_decode(predicted_ids)
+
+        EXPECTED_TRANSCRIPTIONS = [
+            "a man said to the universe sir i exist",
+            "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
+            "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about",
+            "his instant panic was followed by a small sharp blow high on his chest",
+        ]
+        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

utils/check_repo.py

@@ -126,6 +126,7 @@ IGNORE_NON_AUTO_CONFIGURED = [
     "VisualBertForVisualReasoning",
     "VisualBertForQuestionAnswering",
     "VisualBertForMultipleChoice",
+    "TFWav2Vec2ForCTC",
 ]
 
 # This is to make sure the transformers module imported is the one in the repo.