Move pyctcdecode (#14686)

* Move pyctcdecode dep

* Fix doc and last objects

* Quality

* Style

* Ignore this black

docs/source/model_doc/wav2vec2.rst

@@ -77,7 +77,7 @@ Wav2Vec2ProcessorWithLM
 Wav2Vec2 specific outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: transformers.models.wav2vec2.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
+.. autoclass:: transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
     :members: 
 
 .. autoclass:: transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput

src/transformers/__init__.py

@@ -313,7 +313,7 @@ _import_structure = {
         "Wav2Vec2Processor",
         "Wav2Vec2Tokenizer",
     ],
-    "models.wav2vec2_with_lm": [],
+    "models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"],
     "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"],
@@ -475,15 +475,6 @@ else:
         name for name in dir(dummy_speech_objects) if not name.startswith("_")
     ]
 
-if is_pyctcdecode_available():
-    _import_structure["models.wav2vec2_with_lm"].append("Wav2Vec2ProcessorWithLM")
-else:
-    from .utils import dummy_pyctcdecode_objects
-
-    _import_structure["utils.dummy_pyctcdecode_objects"] = [
-        name for name in dir(dummy_pyctcdecode_objects) if not name.startswith("_")
-    ]
-
 if is_sentencepiece_available() and is_speech_available():
     _import_structure["models.speech_to_text"].append("Speech2TextProcessor")
 else:
@@ -2329,6 +2320,7 @@ if TYPE_CHECKING:
         Wav2Vec2Processor,
         Wav2Vec2Tokenizer,
     )
+    from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
     from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer
     from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
     from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
@@ -2472,11 +2464,6 @@ if TYPE_CHECKING:
     else:
         from .utils.dummy_speech_objects import *
 
-    if is_pyctcdecode_available():
-        from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
-    else:
-        from .utils.dummy_pyctcdecode_objects import *
-
     if is_speech_available() and is_sentencepiece_available():
         from .models.speech_to_text import Speech2TextProcessor
     else:

src/transformers/models/wav2vec2_with_lm/__init__.py

@@ -17,19 +17,18 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...file_utils import _LazyModule, is_pyctcdecode_available
+from ...file_utils import _LazyModule
 
 
-_import_structure = {"processing_wav2vec2_with_lm": []}
-
-
-if is_pyctcdecode_available():
-    _import_structure["processing_wav2vec2_with_lm"].append("Wav2Vec2ProcessorWithLM")
+# fmt: off
+_import_structure = {
+    "processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]
+}
+# fmt: on
 
 
 if TYPE_CHECKING:
-    if is_pyctcdecode_available():
-        from .processing_wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
+    from .processing_wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
 else:
     import sys
 

src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py

@@ -19,19 +19,10 @@ import os
 from contextlib import contextmanager
 from dataclasses import dataclass
 from multiprocessing import Pool
-from typing import Iterable, List, Optional, Union
+from typing import TYPE_CHECKING, Iterable, List, Optional, Union
 
 import numpy as np
 
-from pyctcdecode import BeamSearchDecoderCTC
-from pyctcdecode.alphabet import BLANK_TOKEN_PTN, UNK_TOKEN, UNK_TOKEN_PTN
-from pyctcdecode.constants import (
-    DEFAULT_BEAM_WIDTH,
-    DEFAULT_HOTWORD_WEIGHT,
-    DEFAULT_MIN_TOKEN_LOGP,
-    DEFAULT_PRUNE_LOGP,
-)
-
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...file_utils import ModelOutput, requires_backends
 from ...tokenization_utils import PreTrainedTokenizer
@@ -39,6 +30,10 @@ from ..wav2vec2.feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
 from ..wav2vec2.tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
 
 
+if TYPE_CHECKING:
+    from pyctcdecode import BeamSearchDecoderCTC
+
+
 @dataclass
 class Wav2Vec2DecoderWithLMOutput(ModelOutput):
     """
@@ -70,8 +65,10 @@ class Wav2Vec2ProcessorWithLM:
         self,
         feature_extractor: FeatureExtractionMixin,
         tokenizer: PreTrainedTokenizer,
-        decoder: BeamSearchDecoderCTC,
+        decoder: "BeamSearchDecoderCTC",
     ):
+        from pyctcdecode import BeamSearchDecoderCTC
+
         if not isinstance(feature_extractor, Wav2Vec2FeatureExtractor):
             raise ValueError(
                 f"`feature_extractor` has to be of type {Wav2Vec2FeatureExtractor.__class__}, but is {type(feature_extractor)}"
@@ -153,6 +150,8 @@ class Wav2Vec2ProcessorWithLM:
                 :class:`~transformers.PreTrainedTokenizer`
         """
         requires_backends(cls, "pyctcdecode")
+        from pyctcdecode import BeamSearchDecoderCTC
+
         feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
         tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
@@ -183,7 +182,7 @@ class Wav2Vec2ProcessorWithLM:
         return cls(feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=decoder)
 
     @staticmethod
-    def _set_language_model_attribute(decoder: BeamSearchDecoderCTC, attribute: str, value: float):
+    def _set_language_model_attribute(decoder: "BeamSearchDecoderCTC", attribute: str, value: float):
         setattr(decoder.model_container[decoder._model_key], attribute, value)
 
     @property
@@ -192,6 +191,8 @@ class Wav2Vec2ProcessorWithLM:
 
     @staticmethod
     def get_missing_alphabet_tokens(decoder, tokenizer):
+        from pyctcdecode.alphabet import BLANK_TOKEN_PTN, UNK_TOKEN, UNK_TOKEN_PTN
+
         # we need to make sure that all of the tokenizer's except the special tokens
         # are present in the decoder's alphabet. Retrieve missing alphabet token
         # from decoder
@@ -270,6 +271,12 @@ class Wav2Vec2ProcessorWithLM:
             :class:`~transformers.models.wav2vec2.Wav2Vec2DecoderWithLMOutput` or :obj:`tuple`.
 
         """
+        from pyctcdecode.constants import (
+            DEFAULT_BEAM_WIDTH,
+            DEFAULT_HOTWORD_WEIGHT,
+            DEFAULT_MIN_TOKEN_LOGP,
+            DEFAULT_PRUNE_LOGP,
+        )
 
         # set defaults
         beam_width = beam_width if beam_width is not None else DEFAULT_BEAM_WIDTH
@@ -330,6 +337,12 @@ class Wav2Vec2ProcessorWithLM:
             :class:`~transformers.models.wav2vec2.Wav2Vec2DecoderWithLMOutput` or :obj:`tuple`.
 
         """
+        from pyctcdecode.constants import (
+            DEFAULT_BEAM_WIDTH,
+            DEFAULT_HOTWORD_WEIGHT,
+            DEFAULT_MIN_TOKEN_LOGP,
+            DEFAULT_PRUNE_LOGP,
+        )
 
         # set defaults
         beam_width = beam_width if beam_width is not None else DEFAULT_BEAM_WIDTH

src/transformers/utils/dummy_pyctcdecode_objects.py

-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
-
-
-class Wav2Vec2ProcessorWithLM:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pyctcdecode"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pyctcdecode"])