Add Wav2Vec2 & Hubert ForSequenceClassification (#13153)

* Add hubert classifier + tests

* Add hubert classifier + tests

* Dummies for all classification tests

* Wav2Vec2 classifier + ER test

* Fix hubert integration tests

* Add hubert IC

* Pass tests for all classification tasks on Hubert

* Pass all tests + copies

* Move models to the SUPERB org

docs/source/model_doc/hubert.rst

@@ -64,6 +64,14 @@ HubertForCTC
 .. autoclass:: transformers.HubertForCTC
     :members: forward
 
+
+HubertForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.HubertForSequenceClassification
+    :members: forward
+
+
 TFHubertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/wav2vec2.rst

@@ -96,6 +96,14 @@ Wav2Vec2ForCTC
 .. autoclass:: transformers.Wav2Vec2ForCTC
     :members: forward
 
+
+Wav2Vec2ForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.Wav2Vec2ForSequenceClassification
+    :members: forward
+
+
 Wav2Vec2ForPreTraining
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -818,6 +818,7 @@ if is_torch_available():
         [
             "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
             "HubertForCTC",
+            "HubertForSequenceClassification",
             "HubertModel",
             "HubertPreTrainedModel",
         ]
@@ -1128,6 +1129,7 @@ if is_torch_available():
             "Wav2Vec2ForCTC",
             "Wav2Vec2ForMaskedLM",
             "Wav2Vec2ForPreTraining",
+            "Wav2Vec2ForSequenceClassification",
             "Wav2Vec2Model",
             "Wav2Vec2PreTrainedModel",
         ]
@@ -2424,6 +2426,7 @@ if TYPE_CHECKING:
         from .models.hubert import (
             HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             HubertForCTC,
+            HubertForSequenceClassification,
             HubertModel,
             HubertPreTrainedModel,
         )
@@ -2681,6 +2684,7 @@ if TYPE_CHECKING:
             Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2ForPreTraining,
+            Wav2Vec2ForSequenceClassification,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,
         )

src/transformers/models/hubert/__init__.py

@@ -28,6 +28,7 @@ if is_torch_available():
     _import_structure["modeling_hubert"] = [
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
+        "HubertForSequenceClassification",
         "HubertModel",
         "HubertPreTrainedModel",
     ]
@@ -48,6 +49,7 @@ if TYPE_CHECKING:
         from .modeling_hubert import (
             HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             HubertForCTC,
+            HubertForSequenceClassification,
             HubertModel,
             HubertPreTrainedModel,
         )

src/transformers/models/hubert/configuration_hubert.py

@@ -115,6 +115,11 @@ class HubertConfig(PretrainedConfig):
             Whether to zero infinite losses and the associated gradients of ``torch.nn.CTCLoss``. Infinite losses
             mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an
             instance of :class:`~transformers.HubertForCTC`.
+        use_weighted_layer_sum (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
+            instance of :class:`~transformers.HubertForSequenceClassification`.
+        classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
+            Dimensionality of the projection before token mean-pooling for classification.
         gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
             If True, use gradient checkpointing to save memory at the expense of slower backward pass.
 
@@ -165,6 +170,8 @@ class HubertConfig(PretrainedConfig):
         mask_feature_length=10,
         ctc_loss_reduction="sum",
         ctc_zero_infinity=False,
+        use_weighted_layer_sum=False,
+        classifier_proj_size=256,
         gradient_checkpointing=False,
         pad_token_id=0,
         bos_token_id=1,
@@ -197,6 +204,8 @@ class HubertConfig(PretrainedConfig):
         self.vocab_size = vocab_size
         self.do_stable_layer_norm = do_stable_layer_norm
         self.gradient_checkpointing = gradient_checkpointing
+        self.use_weighted_layer_sum = use_weighted_layer_sum
+        self.classifier_proj_size = classifier_proj_size
 
         if (
             (len(self.conv_stride) != self.num_feat_extract_layers)

src/transformers/models/hubert/convert_hubert_original_s3prl_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# 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.
+"""Convert Hubert checkpoint."""
+
+
+import argparse
+
+import torch
+
+from transformers import HubertConfig, HubertForSequenceClassification, Wav2Vec2FeatureExtractor, logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+SUPPORTED_MODELS = ["UtteranceLevel"]
+
+
+@torch.no_grad()
+def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
+        raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}")
+
+    downstream_dict = checkpoint["Downstream"]
+
+    hf_congfig = HubertConfig.from_pretrained(config_path)
+    hf_model = HubertForSequenceClassification.from_pretrained(base_model_name, config=hf_congfig)
+    hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
+        base_model_name, return_attention_mask=True, do_normalize=False
+    )
+
+    if hf_congfig.use_weighted_layer_sum:
+        hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
+
+    hf_model.projector.weight.data = downstream_dict["projector.weight"]
+    hf_model.projector.bias.data = downstream_dict["projector.bias"]
+    hf_model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
+    hf_model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
+
+    hf_feature_extractor.save_pretrained(model_dump_path)
+    hf_model.save_pretrained(model_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
+    )
+    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
+    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
+    args = parser.parse_args()
+    convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

src/transformers/models/hubert/modeling_hubert.py

@@ -20,12 +20,13 @@ import numpy as np
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import CrossEntropyLoss
 
 from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from ...activations import ACT2FN
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput
+from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_hubert import HubertConfig
@@ -735,6 +736,18 @@ class HubertPreTrainedModel(PreTrainedModel):
 
         return input_lengths
 
+    def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
+        output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+        batch_size = attention_mask.shape[0]
+
+        attention_mask = torch.zeros(
+            (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
+        )
+        # these two operations makes sure that all values before the output lengths idxs are attended to
+        attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
+        attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
+        return attention_mask
+
 
 HUBERT_START_DOCSTRING = r"""
     Hubert was proposed in `HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
@@ -904,19 +917,8 @@ class HubertModel(HubertPreTrainedModel):
         extract_features = extract_features.transpose(1, 2)
 
         if attention_mask is not None:
-            # compute real output lengths according to convolution formula
-            output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
-
-            attention_mask = torch.zeros(
-                extract_features.shape[:2], dtype=extract_features.dtype, device=extract_features.device
-            )
-
-            # these two operations makes sure that all values
-            # before the output lengths indices are attended to
-            attention_mask[
-                (torch.arange(attention_mask.shape[0], device=extract_features.device), output_lengths - 1)
-            ] = 1
-            attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
+            # compute reduced attention_mask corresponding to feature vectors
+            attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)
 
         hidden_states = self.feature_projection(extract_features)
         hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
@@ -1070,3 +1072,128 @@ class HubertForCTC(HubertPreTrainedModel):
         return CausalLMOutput(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
+
+
+@add_start_docstrings(
+    """
+    Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
+    SUPERB Keyword Spotting.
+    """,
+    HUBERT_START_DOCSTRING,
+)
+class HubertForSequenceClassification(HubertPreTrainedModel):
+    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.__init__ with Wav2Vec2->Hubert, wav2vec2->hubert
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.hubert = HubertModel(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
+        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
+
+        self.init_weights()
+
+    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor with wav2vec2->hubert
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.hubert.feature_extractor._freeze_parameters()
+
+    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->hubert
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.hubert.parameters():
+            param.requires_grad = False
+
+    @add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Example::
+
+            >>> import torch
+            >>> from transformers import Wav2Vec2FeatureExtractor, HubertForSequenceClassification
+            >>> from datasets import load_dataset
+
+            >>> processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
+            >>> model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ks")
+
+            >>> ds = load_dataset("anton-l/superb_dummy", "ks", split="test")
+
+            >>> input_values = processor(ds["speech"][4], return_tensors="pt").input_values  # Batch size 1
+            >>> logits = model(input_values).logits
+            >>> predicted_class_ids = torch.argmax(logits, dim=-1)
+
+            >>> # compute loss
+            >>> target_label = "down"
+            >>> labels = torch.tensor([model.config.label2id[target_label]])
+
+            >>> loss = model(input_values, labels=labels).loss
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.hubert(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[1]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        hidden_states = self.projector(hidden_states)
+        if attention_mask is None:
+            pooled_output = hidden_states.mean(dim=1)
+        else:
+            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
+            hidden_states[~padding_mask] = 0.0
+            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/wav2vec2/__init__.py

@@ -33,6 +33,7 @@ if is_torch_available():
         "Wav2Vec2ForCTC",
         "Wav2Vec2ForMaskedLM",
         "Wav2Vec2ForPreTraining",
+        "Wav2Vec2ForSequenceClassification",
         "Wav2Vec2Model",
         "Wav2Vec2PreTrainedModel",
     ]
@@ -66,6 +67,7 @@ if TYPE_CHECKING:
             Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2ForPreTraining,
+            Wav2Vec2ForSequenceClassification,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,
         )

src/transformers/models/wav2vec2/configuration_wav2vec2.py

@@ -133,6 +133,11 @@ class Wav2Vec2Config(PretrainedConfig):
             Whether to zero infinite losses and the associated gradients of ``torch.nn.CTCLoss``. Infinite losses
             mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an
             instance of :class:`~transformers.Wav2Vec2ForCTC`.
+        use_weighted_layer_sum (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
+            instance of :class:`~transformers.Wav2Vec2ForSequenceClassification`.
+        classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
+            Dimensionality of the projection before token mean-pooling for classification.
         gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
             If True, use gradient checkpointing to save memory at the expense of slower backward pass.
 
@@ -191,6 +196,8 @@ class Wav2Vec2Config(PretrainedConfig):
         diversity_loss_weight=0.1,
         ctc_loss_reduction="sum",
         ctc_zero_infinity=False,
+        use_weighted_layer_sum=False,
+        classifier_proj_size=256,
         gradient_checkpointing=False,
         pad_token_id=0,
         bos_token_id=1,
@@ -223,6 +230,8 @@ class Wav2Vec2Config(PretrainedConfig):
         self.vocab_size = vocab_size
         self.do_stable_layer_norm = do_stable_layer_norm
         self.gradient_checkpointing = gradient_checkpointing
+        self.use_weighted_layer_sum = use_weighted_layer_sum
+        self.classifier_proj_size = classifier_proj_size
 
         if (
             (len(self.conv_stride) != self.num_feat_extract_layers)

src/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# 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.
+"""Convert Hubert checkpoint."""
+
+
+import argparse
+
+import torch
+
+from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification, logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+SUPPORTED_MODELS = ["UtteranceLevel"]
+
+
+@torch.no_grad()
+def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path):
+    """
+    Copy/paste/tweak model's weights to transformers design.
+    """
+    checkpoint = torch.load(checkpoint_path, map_location="cpu")
+    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
+        raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}")
+
+    downstream_dict = checkpoint["Downstream"]
+
+    hf_congfig = Wav2Vec2Config.from_pretrained(config_path)
+    hf_model = Wav2Vec2ForSequenceClassification.from_pretrained(base_model_name, config=hf_congfig)
+    hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
+        base_model_name, return_attention_mask=True, do_normalize=False
+    )
+
+    if hf_congfig.use_weighted_layer_sum:
+        hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
+
+    hf_model.projector.weight.data = downstream_dict["projector.weight"]
+    hf_model.projector.bias.data = downstream_dict["projector.bias"]
+    hf_model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"]
+    hf_model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"]
+
+    hf_feature_extractor.save_pretrained(model_dump_path)
+    hf_model.save_pretrained(model_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
+    )
+    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
+    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
+    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
+    args = parser.parse_args()
+    convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py

@@ -83,9 +83,6 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
         """
         Every array in the list is normalized to have zero mean and unit variance
         """
-        if isinstance(input_values[0], np.ndarray):
-            input_values = [x.astype(np.float32) for x in input_values]
-
         normed_input_values = [
             (x - np.mean(x[:i])) / np.sqrt(np.var(x[:i]) + 1e-5) for x, i in zip(input_values, input_lengths)
         ]
@@ -205,6 +202,9 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
             padded_input_values = padded_inputs["input_values"]
             input_lengths = [padded_input_values.shape[-1] for _ in range(padded_input_values.shape[0])]
 
+        if isinstance(padded_inputs["input_values"][0], np.ndarray):
+            padded_inputs["input_values"] = [x.astype(np.float32) for x in padded_inputs["input_values"]]
+
         # zero-mean and unit-variance normalization
         if self.do_normalize:
             padded_inputs["input_values"] = self.zero_mean_unit_var_norm(

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -22,6 +22,7 @@ import numpy as np
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
 from ...deepspeed import is_deepspeed_zero3_enabled
@@ -31,7 +32,7 @@ from ...file_utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput
+from ...modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_wav2vec2 import Wav2Vec2Config
@@ -1057,7 +1058,7 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
         extract_features = extract_features.transpose(1, 2)
 
         if attention_mask is not None:
-            # compute reduced attention_mask correponding to feature vectors
+            # compute reduced attention_mask corresponding to feature vectors
             attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)
 
         hidden_states, extract_features = self.feature_projection(extract_features)
@@ -1527,3 +1528,126 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
         return CausalLMOutput(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
+
+
+@add_start_docstrings(
+    """
+    Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
+    SUPERB Keyword Spotting.
+    """,
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wav2vec2 = Wav2Vec2Model(config)
+        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
+        if config.use_weighted_layer_sum:
+            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
+        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
+        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
+
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.wav2vec2.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base model so that its parameters will not
+        be updated during training. Only the classification head will be updated.
+        """
+        for param in self.wav2vec2.parameters():
+            param.requires_grad = False
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Example::
+
+            >>> import torch
+            >>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
+            >>> from datasets import load_dataset
+
+            >>> processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
+            >>> model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
+
+            >>> ds = load_dataset("anton-l/superb_dummy", "ks", split="test")
+
+            >>> input_values = processor(ds["speech"][4], return_tensors="pt").input_values  # Batch size 1
+            >>> logits = model(input_values).logits
+            >>> predicted_class_ids = torch.argmax(logits, dim=-1)
+
+            >>> # compute loss
+            >>> target_label = "down"
+            >>> labels = torch.tensor([model.config.label2id[target_label]])
+
+            >>> loss = model(input_values, labels=labels).loss
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
+
+        outputs = self.wav2vec2(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        # End copy
+
+        if self.config.use_weighted_layer_sum:
+            hidden_states = outputs[2]
+            hidden_states = torch.stack(hidden_states, dim=1)
+            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
+            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
+        else:
+            hidden_states = outputs[0]
+
+        hidden_states = self.projector(hidden_states)
+        if attention_mask is None:
+            pooled_output = hidden_states.mean(dim=1)
+        else:
+            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
+            hidden_states[~padding_mask] = 0.0
+            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
+
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1863,6 +1863,15 @@ class HubertForCTC:
         requires_backends(self, ["torch"])
 
 
+class HubertForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HubertModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
@@ -3473,6 +3482,15 @@ class Wav2Vec2ForPreTraining:
         requires_backends(self, ["torch"])
 
 
+class Wav2Vec2ForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class Wav2Vec2Model:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

tests/test_modeling_hubert.py

@@ -31,7 +31,13 @@ from .test_modeling_common import ModelTesterMixin, _config_zero_init
 if is_torch_available():
     import torch
 
-    from transformers import HubertForCTC, HubertModel, Wav2Vec2Processor
+    from transformers import (
+        HubertForCTC,
+        HubertForSequenceClassification,
+        HubertModel,
+        Wav2Vec2FeatureExtractor,
+        Wav2Vec2Processor,
+    )
     from transformers.models.hubert.modeling_hubert import _compute_mask_indices
 
 
@@ -187,7 +193,32 @@ class HubertModelTester:
         self.parent.assertTrue(isinstance(sum_loss, float))
         self.parent.assertTrue(isinstance(mean_loss, float))
 
-    def check_training(self, config, input_values, *args):
+    def check_seq_classifier_loss(self, config, input_values, *args):
+        model = HubertForSequenceClassification(config=config)
+        model.to(torch_device)
+
+        # make sure that dropout is disabled
+        model.eval()
+
+        input_values = input_values[:3]
+        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
+
+        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
+        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
+
+        # pad input
+        for i in range(len(input_lengths)):
+            input_values[i, input_lengths[i] :] = 0.0
+            attention_mask[i, input_lengths[i] :] = 0
+
+        masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
+        unmasked_loss = model(input_values, labels=labels).loss.item()
+
+        self.parent.assertTrue(isinstance(masked_loss, float))
+        self.parent.assertTrue(isinstance(unmasked_loss, float))
+        self.parent.assertTrue(masked_loss != unmasked_loss)
+
+    def check_ctc_training(self, config, input_values, *args):
         config.ctc_zero_infinity = True
         model = HubertForCTC(config=config)
         model.to(torch_device)
@@ -216,6 +247,29 @@ class HubertModelTester:
 
         loss.backward()
 
+    def check_seq_classifier_training(self, config, input_values, *args):
+        config.ctc_zero_infinity = True
+        model = HubertForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.train()
+
+        # freeze everything but the classification head
+        model.freeze_base_model()
+
+        input_values = input_values[:3]
+
+        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
+        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
+
+        # pad input
+        for i in range(len(input_lengths)):
+            input_values[i, input_lengths[i] :] = 0.0
+
+        loss = model(input_values, labels=labels).loss
+        self.parent.assertFalse(torch.isinf(loss).item())
+
+        loss.backward()
+
     def check_labels_out_of_vocab(self, config, input_values, *args):
         model = HubertForCTC(config)
         model.to(torch_device)
@@ -238,7 +292,7 @@ class HubertModelTester:
 
 @require_torch
 class HubertModelTest(ModelTesterMixin, unittest.TestCase):
-    all_model_classes = (HubertForCTC, HubertModel) if is_torch_available() else ()
+    all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
@@ -258,9 +312,17 @@ class HubertModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_ctc_loss(*config_and_inputs)
 
-    def test_train(self):
+    def test_seq_classifier_loss_inference(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.check_training(*config_and_inputs)
+        self.model_tester.check_seq_classifier_loss(*config_and_inputs)
+
+    def test_ctc_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_ctc_training(*config_and_inputs)
+
+    def test_seq_classifier_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_training(*config_and_inputs)
 
     def test_labels_out_of_vocab(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -371,7 +433,7 @@ class HubertModelTest(ModelTesterMixin, unittest.TestCase):
 
 @require_torch
 class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase):
-    all_model_classes = (HubertForCTC, HubertModel) if is_torch_available() else ()
+    all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
@@ -397,9 +459,17 @@ class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_ctc_loss(*config_and_inputs)
 
-    def test_train(self):
+    def test_seq_classifier_loss_inference(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_loss(*config_and_inputs)
+
+    def test_ctc_train(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.check_training(*config_and_inputs)
+        self.model_tester.check_ctc_training(*config_and_inputs)
+
+    def test_seq_classifier_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_training(*config_and_inputs)
 
     def test_labels_out_of_vocab(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -557,6 +627,13 @@ class HubertModelIntegrationTest(unittest.TestCase):
 
         return ds["speech"][:num_samples]
 
+    def _load_superb(self, task, num_samples):
+        from datasets import load_dataset
+
+        ds = load_dataset("anton-l/superb_dummy", task, split="test")
+
+        return ds[:num_samples]
+
     def test_inference_ctc_batched(self):
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft").to(torch_device)
         processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True)
@@ -579,3 +656,95 @@ class HubertModelIntegrationTest(unittest.TestCase):
             "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
         ]
         self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
+
+    def test_inference_keyword_spotting(self):
+        model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ks").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
+        input_data = self._load_superb("ks", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
+
+        expected_labels = [2, 6, 10, 9]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
+
+    def test_inference_intent_classification(self):
+        model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
+        input_data = self._load_superb("ic", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+
+        predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
+        predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
+        predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)
+
+        expected_labels_action = [1, 0, 4, 3]
+        expected_logits_action = torch.tensor([5.9052, 12.5865, 4.4840, 10.0240], device=torch_device)
+        expected_labels_object = [1, 10, 3, 4]
+        expected_logits_object = torch.tensor([5.5316, 11.7946, 8.1672, 23.2415], device=torch_device)
+        expected_labels_location = [0, 0, 0, 1]
+        expected_logits_location = torch.tensor([5.2053, 8.9577, 10.0447, 8.1481], device=torch_device)
+
+        self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
+        self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
+        self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
+
+        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
+        self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1))
+        self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1))
+        self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))
+
+    def test_inference_speaker_identification(self):
+        model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-sid").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid")
+        input_data = self._load_superb("si", 4)
+
+        output_logits = []
+        with torch.no_grad():
+            for example in input_data["speech"]:
+                input = processor(example, return_tensors="pt", padding=True)
+                output = model(input.input_values.to(torch_device), attention_mask=None)
+                output_logits.append(output.logits[0])
+        output_logits = torch.stack(output_logits)
+        predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)
+
+        expected_labels = [5, 1, 1, 3]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([78231.5547, 123166.6094, 122785.4141, 84851.2969], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10))
+
+    def test_inference_emotion_recognition(self):
+        model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-er").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er")
+        input_data = self._load_superb("er", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
+
+        expected_labels = [1, 1, 2, 2]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        # TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-1))

tests/test_modeling_wav2vec2.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """ Testing suite for the PyTorch Wav2Vec2 model. """
 
-
 import math
 import unittest
 
@@ -36,6 +35,7 @@ if is_torch_available():
         Wav2Vec2ForCTC,
         Wav2Vec2ForMaskedLM,
         Wav2Vec2ForPreTraining,
+        Wav2Vec2ForSequenceClassification,
         Wav2Vec2Model,
         Wav2Vec2Processor,
     )
@@ -194,7 +194,32 @@ class Wav2Vec2ModelTester:
         self.parent.assertTrue(isinstance(sum_loss, float))
         self.parent.assertTrue(isinstance(mean_loss, float))
 
-    def check_training(self, config, input_values, *args):
+    def check_seq_classifier_loss(self, config, input_values, *args):
+        model = Wav2Vec2ForSequenceClassification(config=config)
+        model.to(torch_device)
+
+        # make sure that dropout is disabled
+        model.eval()
+
+        input_values = input_values[:3]
+        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
+
+        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
+        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
+
+        # pad input
+        for i in range(len(input_lengths)):
+            input_values[i, input_lengths[i] :] = 0.0
+            attention_mask[i, input_lengths[i] :] = 0
+
+        masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
+        unmasked_loss = model(input_values, labels=labels).loss.item()
+
+        self.parent.assertTrue(isinstance(masked_loss, float))
+        self.parent.assertTrue(isinstance(unmasked_loss, float))
+        self.parent.assertTrue(masked_loss != unmasked_loss)
+
+    def check_ctc_training(self, config, input_values, *args):
         config.ctc_zero_infinity = True
         model = Wav2Vec2ForCTC(config=config)
         model.to(torch_device)
@@ -223,6 +248,29 @@ class Wav2Vec2ModelTester:
 
         loss.backward()
 
+    def check_seq_classifier_training(self, config, input_values, *args):
+        config.ctc_zero_infinity = True
+        model = Wav2Vec2ForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.train()
+
+        # freeze everything but the classification head
+        model.freeze_base_model()
+
+        input_values = input_values[:3]
+
+        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
+        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
+
+        # pad input
+        for i in range(len(input_lengths)):
+            input_values[i, input_lengths[i] :] = 0.0
+
+        loss = model(input_values, labels=labels).loss
+        self.parent.assertFalse(torch.isinf(loss).item())
+
+        loss.backward()
+
     def check_labels_out_of_vocab(self, config, input_values, *args):
         model = Wav2Vec2ForCTC(config)
         model.to(torch_device)
@@ -246,7 +294,9 @@ class Wav2Vec2ModelTester:
 @require_torch
 class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining) if is_torch_available() else ()
+        (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForSequenceClassification, Wav2Vec2ForPreTraining)
+        if is_torch_available()
+        else ()
     )
     test_pruning = False
     test_headmasking = False
@@ -267,9 +317,17 @@ class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_ctc_loss(*config_and_inputs)
 
-    def test_train(self):
+    def test_seq_classifier_loss_inference(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.check_training(*config_and_inputs)
+        self.model_tester.check_seq_classifier_loss(*config_and_inputs)
+
+    def test_ctc_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_ctc_training(*config_and_inputs)
+
+    def test_seq_classifier_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_training(*config_and_inputs)
 
     def test_labels_out_of_vocab(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -384,7 +442,9 @@ class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase):
 @require_torch
 class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining) if is_torch_available() else ()
+        (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForSequenceClassification, Wav2Vec2ForPreTraining)
+        if is_torch_available()
+        else ()
     )
     test_pruning = False
     test_headmasking = False
@@ -411,9 +471,17 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_ctc_loss(*config_and_inputs)
 
-    def test_train(self):
+    def test_seq_classifier_loss_inference(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_loss(*config_and_inputs)
+
+    def test_ctc_train(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.check_training(*config_and_inputs)
+        self.model_tester.check_ctc_training(*config_and_inputs)
+
+    def test_seq_classifier_train(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.check_seq_classifier_training(*config_and_inputs)
 
     def test_labels_out_of_vocab(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -691,6 +759,13 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         return ds["speech"][:num_samples]
 
+    def _load_superb(self, task, num_samples):
+        from datasets import load_dataset
+
+        ds = load_dataset("anton-l/superb_dummy", task, split="test")
+
+        return ds[:num_samples]
+
     def test_inference_ctc_normal(self):
         model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
         model.to(torch_device)
@@ -795,7 +870,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         # fmt: off
         expected_cosine_sim_masked = torch.tensor(
-            [0.7458, 0.7188, 0.6418, 0.3729, 0.3741, 0.3694, 0.3110, 0.2257, 0.4403, 0.5415, 0.3950, 0.3701, 0.8831, 0.8613, 0.5229, 0.6696, 0.7206, 0.7877, 0.6758, 0.8746, 0.6596, 0.6282, 0.6178, 0.5839, 0.5926, 0.6651, 0.4635, 0.6332, 0.6572, 0.8776, 0.4999, 0.7001, 0.7257, 0.5098, 0.6229, 0.4566, 0.5261, 0.6363, 0.5371, 0.6997],
+            [0.7458, 0.7188, 0.6418, 0.3729, 0.3741, 0.3694, 0.3110, 0.2257, 0.4403, 0.5415, 0.3950, 0.3701, 0.8831,
+             0.8613, 0.5229, 0.6696, 0.7206, 0.7877, 0.6758, 0.8746, 0.6596, 0.6282, 0.6178, 0.5839, 0.5926, 0.6651,
+             0.4635, 0.6332, 0.6572, 0.8776, 0.4999, 0.7001, 0.7257, 0.5098, 0.6229, 0.4566, 0.5261, 0.6363, 0.5371,
+             0.6997],
             device=torch_device,
         )
         # fmt: on
@@ -913,3 +991,92 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
         expected_loss = 62.5170
 
         self.assertTrue(abs(outputs.loss.item() - expected_loss) < 1e-3)
+
+    def test_inference_keyword_spotting(self):
+        model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
+        input_data = self._load_superb("ks", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
+
+        expected_labels = [7, 6, 10, 9]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([6.1186, 11.8961, 10.2931, 6.0898], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
+
+    def test_inference_intent_classification(self):
+        model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ic")
+        input_data = self._load_superb("ic", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+
+        predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
+        predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
+        predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)
+
+        expected_labels_action = [0, 0, 2, 3]
+        expected_logits_action = torch.tensor([0.4568, 11.0848, 1.6621, 9.3841], device=torch_device)
+        expected_labels_object = [3, 10, 3, 4]
+        expected_logits_object = torch.tensor([1.5322, 10.7094, 5.2469, 22.1318], device=torch_device)
+        expected_labels_location = [0, 0, 0, 1]
+        expected_logits_location = torch.tensor([1.5335, 6.5096, 10.5704, 11.0569], device=torch_device)
+
+        self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
+        self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
+        self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
+
+        self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=1e-2))
+        self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=1e-2))
+        self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=1e-2))
+
+    def test_inference_speaker_identification(self):
+        model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid")
+        input_data = self._load_superb("si", 4)
+
+        output_logits = []
+        with torch.no_grad():
+            for example in input_data["speech"]:
+                input = processor(example, return_tensors="pt", padding=True)
+                output = model(input.input_values.to(torch_device), attention_mask=None)
+                output_logits.append(output.logits[0])
+        output_logits = torch.stack(output_logits)
+        predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)
+
+        expected_labels = [251, 1, 1, 3]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([37.5627, 71.6362, 64.2419, 31.7778], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
+
+    def test_inference_emotion_recognition(self):
+        model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er").to(torch_device)
+        processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er")
+        input_data = self._load_superb("er", 4)
+        inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.to(torch_device)
+        attention_mask = inputs.attention_mask.to(torch_device)
+        with torch.no_grad():
+            outputs = model(input_values, attention_mask=attention_mask)
+        predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
+
+        expected_labels = [1, 1, 2, 2]
+        # s3prl logits for the same batch
+        expected_logits = torch.tensor([2.1722, 3.0779, 8.0287, 6.6797], device=torch_device)
+
+        self.assertListEqual(predicted_ids.tolist(), expected_labels)
+        self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))

utils/check_repo.py

@@ -122,6 +122,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "TFRagTokenForGeneration",
     "Wav2Vec2ForCTC",
     "HubertForCTC",
+    "Wav2Vec2ForSequenceClassification",
+    "HubertForSequenceClassification",
     "XLMForQuestionAnswering",
     "XLNetForQuestionAnswering",
     "SeparableConv1D",