[AutoProcessor] Add Wav2Vec2WithLM & small fix (#14675)

* [AutoProcessor] Add Wav2Vec2WithLM & small fix

* revert line removal

* Update src/transformers/__init__.py

* add test

* up

* up

* small fix

src/transformers/__init__.py

@@ -312,6 +312,7 @@ _import_structure = {
         "Wav2Vec2Processor",
         "Wav2Vec2Tokenizer",
     ],
+    "models.wav2vec2_with_lm": [],
     "models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"],
     "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
     "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"],
@@ -474,7 +475,7 @@ else:
     ]
 
 if is_pyctcdecode_available():
-    _import_structure["models.wav2vec2"].append("Wav2Vec2ProcessorWithLM")
+    _import_structure["models.wav2vec2_with_lm"].append("Wav2Vec2ProcessorWithLM")
 else:
     from .utils import dummy_pyctcdecode_objects
 
@@ -2470,7 +2471,7 @@ if TYPE_CHECKING:
         from .utils.dummy_speech_objects import *
 
     if is_pyctcdecode_available():
-        from .models.wav2vec2 import Wav2Vec2ProcessorWithLM
+        from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
     else:
         from .utils.dummy_pyctcdecode_objects import *
 

src/transformers/models/__init__.py

@@ -107,6 +107,7 @@ from . import (
     visual_bert,
     vit,
     wav2vec2,
+    wav2vec2_with_lm,
     xlm,
     xlm_prophetnet,
     xlm_roberta,

src/transformers/models/auto/processing_auto.py

@@ -39,6 +39,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
         ("speech_to_text_2", "Speech2Text2Processor"),
         ("trocr", "TrOCRProcessor"),
         ("wav2vec2", "Wav2Vec2Processor"),
+        ("wav2vec2_with_lm", "Wav2Vec2ProcessorWithLM"),
         ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
     ]
 )
@@ -145,6 +146,9 @@ class AutoProcessor:
             key: kwargs[key] for key in ["revision", "use_auth_token", "local_files_only"] if key in kwargs
         }
         model_files = get_list_of_files(pretrained_model_name_or_path, **get_list_of_files_kwargs)
+        # strip to file name
+        model_files = [f.split("/")[-1] for f in model_files]
+
         if FEATURE_EXTRACTOR_NAME in model_files:
             config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
             if "processor_class" in config_dict:

src/transformers/models/wav2vec2/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_flax_available, is_pyctcdecode_available, is_tf_available, is_torch_available
+from ...file_utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -27,8 +27,6 @@ _import_structure = {
     "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
 }
 
-if is_pyctcdecode_available():
-    _import_structure["processing_wav2vec2_with_lm"] = ["Wav2Vec2ProcessorWithLM"]
 
 if is_torch_available():
     _import_structure["modeling_wav2vec2"] = [
@@ -64,9 +62,6 @@ if TYPE_CHECKING:
     from .processing_wav2vec2 import Wav2Vec2Processor
     from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer
 
-    if is_pyctcdecode_available():
-        from .processing_wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
-
     if is_torch_available():
         from .modeling_wav2vec2 import (
             WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/models/wav2vec2_with_lm/__init__.py

@@ -0,0 +1,36 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021 The HuggingFace 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.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _LazyModule, is_pyctcdecode_available
+
+
+_import_structure = {}
+
+
+if is_pyctcdecode_available():
+    _import_structure["processing_wav2vec2_with_lm"] = ["Wav2Vec2ProcessorWithLM"]
+
+
+if TYPE_CHECKING:
+    if is_pyctcdecode_available():
+        from .processing_wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py

@@ -35,8 +35,8 @@ from pyctcdecode.constants import (
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...file_utils import ModelOutput, requires_backends
 from ...tokenization_utils import PreTrainedTokenizer
-from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
-from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
+from ..wav2vec2.feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
+from ..wav2vec2.tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
 
 
 @dataclass
@@ -159,6 +159,9 @@ class Wav2Vec2ProcessorWithLM:
         if os.path.isdir(pretrained_model_name_or_path):
             decoder = BeamSearchDecoderCTC.load_from_dir(pretrained_model_name_or_path)
         else:
+            # BeamSearchDecoderCTC has no auto class
+            kwargs.pop("_from_auto", None)
+
             decoder = BeamSearchDecoderCTC.load_from_hf_hub(pretrained_model_name_or_path, **kwargs)
 
         # set language model attributes

tests/fixtures/dummy_feature_extractor_config.json

@@ -1,3 +1,4 @@
 {
-  "feature_extractor_type": "Wav2Vec2FeatureExtractor"
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "processor_class": "Wav2Vec2Processor"
 }

tests/test_processor_auto.py

@@ -16,15 +16,16 @@
 import os
 import tempfile
 import unittest
+from shutil import copyfile
 
 from transformers import AutoProcessor, Wav2Vec2Config, Wav2Vec2Processor
+from transformers.file_utils import FEATURE_EXTRACTOR_NAME
 
 
-SAMPLE_PROCESSOR_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
 SAMPLE_PROCESSOR_CONFIG = os.path.join(
     os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
 )
-SAMPLE_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy-config.json")
+SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/vocab.json")
 
 
 class AutoFeatureExtractorTest(unittest.TestCase):
@@ -32,7 +33,7 @@ class AutoFeatureExtractorTest(unittest.TestCase):
         processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
         self.assertIsInstance(processor, Wav2Vec2Processor)
 
-    def test_processor_from_local_directory_from_config(self):
+    def test_processor_from_local_directory_from_repo(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             model_config = Wav2Vec2Config()
             processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
@@ -44,3 +45,13 @@ class AutoFeatureExtractorTest(unittest.TestCase):
             processor = AutoProcessor.from_pretrained(tmpdirname)
 
         self.assertIsInstance(processor, Wav2Vec2Processor)
+
+    def test_processor_from_local_directory_from_extractor_config(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            # copy relevant files
+            copyfile(SAMPLE_PROCESSOR_CONFIG, os.path.join(tmpdirname, FEATURE_EXTRACTOR_NAME))
+            copyfile(SAMPLE_VOCAB, os.path.join(tmpdirname, "vocab.json"))
+
+            processor = AutoProcessor.from_pretrained(tmpdirname)
+
+        self.assertIsInstance(processor, Wav2Vec2Processor)

tests/test_processor_wav2vec2_with_lm.py

@@ -31,7 +31,7 @@ from .test_feature_extraction_wav2vec2 import floats_list
 
 if is_pyctcdecode_available():
     from pyctcdecode import BeamSearchDecoderCTC
-    from transformers.models.wav2vec2 import Wav2Vec2ProcessorWithLM
+    from transformers.models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
 
 
 @require_pyctcdecode

utils/check_inits.py

@@ -27,6 +27,8 @@ _re_backend = re.compile(r"is\_([a-z_]*)_available()")
 _re_import_struct_key_value = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
 # Catches a line if is_foo_available
 _re_test_backend = re.compile(r"^\s*if\s+is\_[a-z_]*\_available\(\)")
+# Catches a line _import_struct["bla"] = ["foo"]
+_re_import_struct_equal_one = re.compile(r'^\s*_import_structure\["\S*"\]\ = "\[(\S*)\]"')
 # Catches a line _import_struct["bla"].append("foo")
 _re_import_struct_add_one = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
 # Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
@@ -88,7 +90,9 @@ def parse_init(init_file):
             # Until we unindent, add backend objects to the list
             while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4):
                 line = lines[line_index]
-                if _re_import_struct_add_one.search(line) is not None:
+                if _re_import_struct_equal_one.search(line) is not None:
+                    objects.append(_re_import_struct_equal_one.search(line).groups()[0])
+                elif _re_import_struct_add_one.search(line) is not None:
                     objects.append(_re_import_struct_add_one.search(line).groups()[0])
                 elif _re_import_struct_add_many.search(line) is not None:
                     imports = _re_import_struct_add_many.search(line).groups()[0].split(", ")