Deprecate Wav2Vec2ForMaskedLM and add Wav2Vec2ForCTC (#10089)

* add wav2vec2CTC and deprecate for maskedlm

* remove from docs

docs/source/model_doc/wav2vec2.rst

@@ -58,8 +58,8 @@ Wav2Vec2Model
     :members: forward
 
 
-Wav2Vec2ForMaskedLM
+Wav2Vec2ForCTC
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: transformers.Wav2Vec2ForMaskedLM
+.. autoclass:: transformers.Wav2Vec2ForCTC
     :members: forward

src/transformers/__init__.py

@@ -367,6 +367,7 @@
     _import_structure["models.wav2vec2"].extend(
         [
             "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "Wav2Vec2ForCTC",
             "Wav2Vec2ForMaskedLM",
             "Wav2Vec2Model",
             "Wav2Vec2PreTrainedModel",
@@ -1813,6 +1814,7 @@
         )
         from .models.wav2vec2 import (
             WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,

src/transformers/models/wav2vec2/__init__.py

@@ -29,6 +29,7 @@
     _import_structure["modeling_wav2vec2"] = [
         "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Wav2Vec2ForMaskedLM",
+        "Wav2Vec2ForCTC",
         "Wav2Vec2Model",
         "Wav2Vec2PreTrainedModel",
     ]
@@ -41,6 +42,7 @@
     if is_torch_available():
         from .modeling_wav2vec2 import (
             WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Wav2Vec2ForCTC,
             Wav2Vec2ForMaskedLM,
             Wav2Vec2Model,
             Wav2Vec2PreTrainedModel,

src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py

@@ -20,7 +20,7 @@
 import fairseq
 import torch
 
-from transformers import Wav2Vec2Config, Wav2Vec2ForMaskedLM, logging
+from transformers import Wav2Vec2Config, Wav2Vec2ForCTC, logging
 
 
 logging.set_verbosity_info()
@@ -141,7 +141,7 @@ def convert_wav2vec2_checkpoint(checkpoint_path, pytorch_dump_folder_path, dict_
     """
     Copy/paste/tweak model's weights to transformers design.
     """
-    hf_wav2vec = Wav2Vec2ForMaskedLM(Wav2Vec2Config())
+    hf_wav2vec = Wav2Vec2ForCTC(Wav2Vec2Config())
 
     model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path], arg_overrides={"data": dict_path}

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -15,6 +15,7 @@
 """ PyTorch Wav2Vec2 model. """
 
 
+import warnings
 from typing import Optional, Tuple
 
 import torch
@@ -24,7 +25,7 @@
 
 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, MaskedLMOutput
+from ...modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_wav2vec2 import Wav2Vec2Config
@@ -665,6 +666,10 @@ class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
+        warnings.warn(
+            "The class `Wav2Vec2ForMaskedLM` is deprecated. Please use `Wav2Vec2ForCTC` instead.", FutureWarning
+        )
+
         self.wav2vec2 = Wav2Vec2Model(config)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
@@ -729,3 +734,77 @@ def forward(
             return output
 
         return MaskedLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+
+
+@add_start_docstrings(
+    """Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). """,
+    WAV_2_VEC_2_START_DOCSTRING,
+)
+class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_values,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+    ):
+        r"""
+        labels (:obj:`Float.LongTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            TODO(PVP): Fill out when adding training
+
+        Returns:
+
+        Example::
+
+            >>> from transformers import Wav2Vec2Tokenizer, Wav2Vec2Model
+            >>> from datasets import load_dataset
+            >>> import soundfile as sf
+
+            >>> tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+            >>> model = Wav2Vec2ForCTC.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 = tokenizer(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
+            >>> logits = model(input_values).logits
+
+            >>> predicted_ids = torch.argmax(logits, dim=-1)
+            >>> transcription = tokenizer.decode(predicted_ids[0])
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.wav2vec2(
+            input_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return output
+
+        return CausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)

src/transformers/utils/dummy_pt_objects.py

@@ -2229,6 +2229,11 @@ def load_tf_weights_in_transfo_xl(*args, **kwargs):
 WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class Wav2Vec2ForCTC:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
 class Wav2Vec2ForMaskedLM:
     def __init__(self, *args, **kwargs):
         requires_pytorch(self)

tests/test_modeling_wav2vec2.py

@@ -29,7 +29,7 @@
 if is_torch_available():
     import torch
 
-    from transformers import Wav2Vec2Config, Wav2Vec2ForMaskedLM, Wav2Vec2Model, Wav2Vec2Tokenizer
+    from transformers import Wav2Vec2Config, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2Model, Wav2Vec2Tokenizer
 
 
 class Wav2Vec2ModelTester:
@@ -204,7 +204,7 @@ def test_model_from_pretrained(self):
 
 @require_torch
 class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
-    all_model_classes = (Wav2Vec2Model, Wav2Vec2ForMaskedLM) if is_torch_available() else ()
+    all_model_classes = (Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForCTC) if is_torch_available() else ()
     test_pruning = False
     test_headmasking = False
     test_torchscript = False
@@ -289,7 +289,7 @@ def map_to_array(batch):
         return ds["speech"][:num_samples]
 
     def test_inference_masked_lm_normal(self):
-        model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-base-960h")
+        model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
         model.to(torch_device)
         tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
 
@@ -307,7 +307,7 @@ def test_inference_masked_lm_normal(self):
         self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
 
     def test_inference_masked_lm_normal_batched(self):
-        model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-base-960h")
+        model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
         model.to(torch_device)
         tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
 
@@ -330,7 +330,7 @@ def test_inference_masked_lm_normal_batched(self):
         self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
 
     def test_inference_masked_lm_robust_batched(self):
-        model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(torch_device)
+        model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(torch_device)
         tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
 
         input_speech = self._load_datasamples(4)

utils/check_repo.py

@@ -118,6 +118,7 @@
     "TFMT5EncoderModel",
     "TFOpenAIGPTDoubleHeadsModel",
     "TFT5EncoderModel",
+    "Wav2Vec2ForCTC",
     "XLMForQuestionAnswering",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
@@ -370,6 +371,7 @@ def find_all_documented_objects():
     "TFBartPretrainedModel",
     "TextDataset",
     "TextDatasetForNextSentencePrediction",
+    "Wav2Vec2ForMaskedLM",
     "glue_compute_metrics",
     "glue_convert_examples_to_features",
     "glue_output_modes",