Add Wav2Vec2 & Hubert ForSequenceClassification

docs/source/model_doc/hubert.rst

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

src/transformers/__init__.py

@@ -818,6 +818,7 @@
         [
             "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
             "HubertForCTC",
+            "HubertForSequenceClassification",
             "HubertModel",
             "HubertPreTrainedModel",
         ]
@@ -2423,6 +2424,7 @@
         from .models.hubert import (
             HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             HubertForCTC,
+            HubertForSequenceClassification,
             HubertModel,
             HubertPreTrainedModel,
         )

src/transformers/models/hubert/__init__.py

@@ -28,6 +28,7 @@
     _import_structure["modeling_hubert"] = [
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
+        "HubertForSequenceClassification",
         "HubertModel",
         "HubertPreTrainedModel",
     ]
@@ -48,6 +49,7 @@
         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 training 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 @@ def __init__(
         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 @@ def __init__(
         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

@@ -0,0 +1,77 @@
+# 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,
+    Wav2Vec2CTCTokenizer,
+    Wav2Vec2FeatureExtractor,
+    Wav2Vec2Processor,
+    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)
+    # TODO: remove the need for a tokenizer
+    tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+    hf_processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+    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_processor.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 torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 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
@@ -1070,3 +1071,123 @@ def forward(
         return CausalLMOutput(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
+
+
+@add_start_docstrings(
+    """
+    Hubert Model with a sequence classification/regression head on top (a linear layer over the pooled output) for
+    tasks like SUPERB.
+    """,
+    HUBERT_START_DOCSTRING,
+)
+class HubertForSequenceClassification(HubertPreTrainedModel):
+    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()
+
+    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.hubert.feature_extractor._freeze_parameters()
+
+    def freeze_base_model(self):
+        """
+        Calling this function will disable the gradient computation for the base Hubert model so that its parameter
+        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:
+
+        TODO: Usage example with Keyword Spotting
+        """
+
+        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:
+            output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
+            pooled_output = hidden_states.sum(dim=1) / output_lengths.view(-1, 1)
+
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.config.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.config.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        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/utils/dummy_pt_objects.py

@@ -1863,6 +1863,15 @@ def __init__(self, *args, **kwargs):
         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"])