Auto feature extractor (huggingface#11097)

* AutoFeatureExtractor

* Init and first tests

* Tests

* Damn you gitignore

* Quality

* Defensive test for when not all backends are here

* Use pattern for Speech2Text models

Author: sgugger

.gitignore

@@ -9,8 +9,7 @@ __pycache__/
 *.so
 
 # tests and logs
-tests/fixtures/*
-!tests/fixtures/sample_text_no_unicode.txt
+tests/fixtures/cached_*_text.txt
 logs/
 lightning_logs/
 lang_code_data/

docs/source/model_doc/auto.rst

@@ -44,6 +44,13 @@ AutoTokenizer
     :members:
 
 
+AutoFeatureExtractor
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.AutoFeatureExtractor
+    :members:
+
+
 AutoModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

src/transformers/__init__.py

@@ -45,6 +45,7 @@
     _BaseLazyModule,
     is_flax_available,
     is_sentencepiece_available,
+    is_speech_available,
     is_tf_available,
     is_tokenizers_available,
     is_torch_available,
@@ -102,6 +103,7 @@
         "is_py3nvml_available",
         "is_sentencepiece_available",
         "is_sklearn_available",
+        "is_speech_available",
         "is_tf_available",
         "is_tokenizers_available",
         "is_torch_available",
@@ -133,9 +135,11 @@
     "models.auto": [
         "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "CONFIG_MAPPING",
+        "FEATURE_EXTRACTOR_MAPPING",
         "MODEL_NAMES_MAPPING",
         "TOKENIZER_MAPPING",
         "AutoConfig",
+        "AutoFeatureExtractor",
         "AutoTokenizer",
     ],
     "models.bart": ["BartConfig", "BartTokenizer"],
@@ -202,7 +206,6 @@
     "models.speech_to_text": [
         "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "Speech2TextConfig",
-        "Speech2TextFeatureExtractor",
     ],
     "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"],
     "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"],
@@ -288,7 +291,6 @@
     _import_structure["models.pegasus"].append("PegasusTokenizer")
     _import_structure["models.reformer"].append("ReformerTokenizer")
     _import_structure["models.speech_to_text"].append("Speech2TextTokenizer")
-    _import_structure["models.speech_to_text"].append("Speech2TextProcessor")
     _import_structure["models.t5"].append("T5Tokenizer")
     _import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer")
     _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer")
@@ -339,13 +341,28 @@
 
     if is_sentencepiece_available():
         _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"]
+
 else:
     from .utils import dummy_tokenizers_objects
 
     _import_structure["utils.dummy_tokenizers_objects"] = [
         name for name in dir(dummy_tokenizers_objects) if not name.startswith("_")
     ]
 
+# Speech-specific objects
+if is_speech_available():
+    _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor")
+
+    if is_sentencepiece_available():
+        _import_structure["models.speech_to_text"].append("Speech2TextProcessor")
+
+else:
+    from .utils import dummy_speech_objects
+
+    _import_structure["utils.dummy_speech_objects"] = [
+        name for name in dir(dummy_speech_objects) if not name.startswith("_")
+    ]
+
 # Vision-specific objects
 if is_vision_available():
     _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"]
@@ -1394,6 +1411,7 @@
         is_py3nvml_available,
         is_sentencepiece_available,
         is_sklearn_available,
+        is_speech_available,
         is_tf_available,
         is_tokenizers_available,
         is_torch_available,
@@ -1429,9 +1447,11 @@
     from .models.auto import (
         ALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CONFIG_MAPPING,
+        FEATURE_EXTRACTOR_MAPPING,
         MODEL_NAMES_MAPPING,
         TOKENIZER_MAPPING,
         AutoConfig,
+        AutoFeatureExtractor,
         AutoTokenizer,
     )
     from .models.bart import BartConfig, BartTokenizer
@@ -1494,11 +1514,7 @@
     from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
     from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer
     from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer
-    from .models.speech_to_text import (
-        SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-        Speech2TextConfig,
-        Speech2TextFeatureExtractor,
-    )
+    from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
     from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
     from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
     from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer
@@ -1585,7 +1601,7 @@
         from .models.mt5 import MT5Tokenizer
         from .models.pegasus import PegasusTokenizer
         from .models.reformer import ReformerTokenizer
-        from .models.speech_to_text import Speech2TextProcessor, Speech2TextTokenizer
+        from .models.speech_to_text import Speech2TextTokenizer
         from .models.t5 import T5Tokenizer
         from .models.xlm_prophetnet import XLMProphetNetTokenizer
         from .models.xlm_roberta import XLMRobertaTokenizer
@@ -1627,9 +1643,19 @@
 
         if is_sentencepiece_available():
             from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer
+
     else:
         from .utils.dummy_tokenizers_objects import *
 
+    if is_speech_available():
+        from .models.speech_to_text import Speech2TextFeatureExtractor
+
+        if is_sentencepiece_available():
+            from .models.speech_to_text import Speech2TextProcessor
+
+    else:
+        from .utils.dummy_speech_objects import *
+
     if is_vision_available():
         from .image_utils import ImageFeatureExtractionMixin
         from .models.vit import ViTFeatureExtractor

src/transformers/dependency_versions_table.py

@@ -43,6 +43,7 @@
     "sphinx-copybutton": "sphinx-copybutton",
     "sphinx-markdown-tables": "sphinx-markdown-tables",
     "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
+    "sphinxext-opengraph": "sphinxext-opengraph==0.4.1",
     "sphinx": "sphinx==3.2.1",
     "starlette": "starlette",
     "tensorflow-cpu": "tensorflow-cpu>=2.3",

src/transformers/feature_extraction_utils.py

@@ -325,6 +325,13 @@ def get_feature_extractor_dict(
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
 
+        from_pipeline = kwargs.pop("_from_pipeline", None)
+        from_auto_class = kwargs.pop("_from_auto", False)
+
+        user_agent = {"file_type": "feature extractor", "from_auto_class": from_auto_class}
+        if from_pipeline is not None:
+            user_agent["using_pipeline"] = from_pipeline
+
         if is_offline_mode() and not local_files_only:
             logger.info("Offline mode: forcing local_files_only=True")
             local_files_only = True
@@ -349,6 +356,7 @@ def get_feature_extractor_dict(
                 resume_download=resume_download,
                 local_files_only=local_files_only,
                 use_auth_token=use_auth_token,
+                user_agent=user_agent,
             )
             # Load feature_extractor dict
             with open(resolved_feature_extractor_file, "r", encoding="utf-8") as reader:
@@ -426,6 +434,7 @@ def to_dict(self) -> Dict[str, Any]:
             :obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this feature extractor instance.
         """
         output = copy.deepcopy(self.__dict__)
+        output["feature_extractor_type"] = self.__class__.__name__
 
         return output
 

src/transformers/file_utils.py

@@ -397,6 +397,11 @@ def is_torchaudio_available():
     return _torchaudio_available
 
 
+def is_speech_available():
+    # For now this depends on torchaudio but the exact dependency might evolve in the future.
+    return _torchaudio_available
+
+
 def torch_only_method(fn):
     def wrapper(*args, **kwargs):
         if not _torch_available:
@@ -513,6 +518,13 @@ def wrapper(*args, **kwargs):
 """
 
 
+# docstyle-ignore
+SPEECH_IMPORT_ERROR = """
+{0} requires the torchaudio library but it was not found in your environment. You can install it with pip:
+`pip install torchaudio`
+"""
+
+
 # docstyle-ignore
 VISION_IMPORT_ERROR = """
 {0} requires the PIL library but it was not found in your environment. You can install it with pip:
@@ -586,6 +598,12 @@ def requires_scatter(obj):
         raise ImportError(SCATTER_IMPORT_ERROR.format(name))
 
 
+def requires_speech(obj):
+    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
+    if not is_speech_available():
+        raise ImportError(SPEECH_IMPORT_ERROR.format(name))
+
+
 def requires_vision(obj):
     name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
     if not is_vision_available():

src/transformers/models/auto/__init__.py

@@ -23,6 +23,7 @@
 
 _import_structure = {
     "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
+    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
     "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
 }
 
@@ -104,6 +105,7 @@
 
 if TYPE_CHECKING:
     from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
+    from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
     from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
 
     if is_torch_available():