Add TFHubertModel (#12206)

* TFHubert

* Update with TFWav2Vec Bug Fixes

* Add OOV Error

* Feedback changes

* Fix kwargs call

docs/source/index.rst

@@ -355,7 +355,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Hubert            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/hubert.rst

@@ -63,3 +63,16 @@ HubertForCTC
 
 .. autoclass:: transformers.HubertForCTC
     :members: forward
+
+TFHubertModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFHubertModel
+    :members: call
+
+
+TFHubertForCTC
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFHubertForCTC
+    :members: call

src/transformers/__init__.py

@@ -1343,6 +1343,14 @@ if is_tf_available():
             "TFGPT2PreTrainedModel",
         ]
     )
+    _import_structure["models.hubert"].extend(
+        [
+            "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFHubertForCTC",
+            "TFHubertModel",
+            "TFHubertPreTrainedModel",
+        ]
+    )
     _import_structure["models.layoutlm"].extend(
         [
             "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2804,6 +2812,12 @@ if TYPE_CHECKING:
             TFGPT2Model,
             TFGPT2PreTrainedModel,
         )
+        from .models.hubert import (
+            TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFHubertForCTC,
+            TFHubertModel,
+            TFHubertPreTrainedModel,
+        )
         from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
         from .models.longformer import (
             TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/models/auto/modeling_tf_auto.py

@@ -101,6 +101,7 @@ from ..funnel.modeling_tf_funnel import (
     TFFunnelModel,
 )
 from ..gpt2.modeling_tf_gpt2 import TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model
+from ..hubert.modeling_tf_hubert import TFHubertModel
 from ..layoutlm.modeling_tf_layoutlm import (
     TFLayoutLMForMaskedLM,
     TFLayoutLMForSequenceClassification,
@@ -204,6 +205,7 @@ from .configuration_auto import (
     FlaubertConfig,
     FunnelConfig,
     GPT2Config,
+    HubertConfig,
     LayoutLMConfig,
     LEDConfig,
     LongformerConfig,
@@ -266,6 +268,7 @@ TF_MODEL_MAPPING = OrderedDict(
         (BlenderbotConfig, TFBlenderbotModel),
         (BlenderbotSmallConfig, TFBlenderbotSmallModel),
         (Wav2Vec2Config, TFWav2Vec2Model),
+        (HubertConfig, TFHubertModel),
     ]
 )
 

src/transformers/models/hubert/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_torch_available
+from ...file_utils import _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -33,6 +33,14 @@ if is_torch_available():
     ]
 
 
+if is_tf_available():
+    _import_structure["modeling_tf_hubert"] = [
+        "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFHubertForCTC",
+        "TFHubertModel",
+        "TFHubertPreTrainedModel",
+    ]
+
 if TYPE_CHECKING:
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
 
@@ -44,6 +52,14 @@ if TYPE_CHECKING:
             HubertPreTrainedModel,
         )
 
+    if is_tf_available():
+        from .modeling_tf_hubert import (
+            TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFHubertForCTC,
+            TFHubertModel,
+            TFHubertPreTrainedModel,
+        )
+
 
 else:
     import sys

src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py

@@ -83,13 +83,6 @@ def input_values_processing(func, config, input_values, **kwargs):
     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
@@ -1398,7 +1391,6 @@ class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         training: bool = False,
-        **kwargs: Any,
     ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
         """
 
@@ -1438,7 +1430,6 @@ class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             training=training,
-            kwargs_call=kwargs,
         )
 
         inputs["output_hidden_states"] = (
@@ -1505,7 +1496,6 @@ class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
         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`):
@@ -1561,7 +1551,6 @@ class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             training=training,
-            kwargs_call=kwargs,
         )
 
         outputs = self.wav2vec2(

src/transformers/utils/dummy_tf_objects.py

@@ -1014,6 +1014,32 @@ class TFGPT2PreTrainedModel:
         requires_backends(cls, ["tf"])
 
 
+TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFHubertForCTC:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFHubertModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
+class TFHubertPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["tf"])
+
+
 class TFLEDForConditionalGeneration:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tf"])

utils/check_repo.py

@@ -130,6 +130,7 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "VisualBertForQuestionAnswering",
     "VisualBertForMultipleChoice",
     "TFWav2Vec2ForCTC",
+    "TFHubertForCTC",
 ]
 
 # This is to make sure the transformers module imported is the one in the repo.