Fix audio feature extractor deps (#24636)

* Fix audio feature extractor deps

* use audio utils window over torch window

src/transformers/__init__.py

@@ -285,6 +285,7 @@ _import_structure = {
     "models.encodec": [
         "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "EncodecConfig",
+        "EncodecFeatureExtractor",
     ],
     "models.encoder_decoder": ["EncoderDecoderConfig"],
     "models.ernie": [
@@ -388,7 +389,7 @@ _import_structure = {
     "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig", "MaskFormerSwinConfig"],
     "models.mbart": ["MBartConfig"],
     "models.mbart50": [],
-    "models.mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTProcessor"],
+    "models.mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTFeatureExtractor", "MCTCTProcessor"],
     "models.mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig"],
     "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
     "models.megatron_gpt2": [],
@@ -481,6 +482,7 @@ _import_structure = {
         "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
         "SpeechT5Config",
+        "SpeechT5FeatureExtractor",
         "SpeechT5HifiGanConfig",
         "SpeechT5Processor",
     ],
@@ -519,6 +521,7 @@ _import_structure = {
     "models.tvlt": [
         "TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "TvltConfig",
+        "TvltFeatureExtractor",
         "TvltProcessor",
     ],
     "models.umt5": [],
@@ -843,11 +846,7 @@ except OptionalDependencyNotAvailable:
     ]
 else:
     _import_structure["models.audio_spectrogram_transformer"].append("ASTFeatureExtractor")
-    _import_structure["models.encodec"].append("EncodecFeatureExtractor")
-    _import_structure["models.mctct"].append("MCTCTFeatureExtractor")
     _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor")
-    _import_structure["models.speecht5"].append("SpeechT5FeatureExtractor")
-    _import_structure["models.tvlt"].append("TvltFeatureExtractor")
 
 # Tensorflow-text-specific objects
 try:
@@ -4170,6 +4169,7 @@ if TYPE_CHECKING:
     from .models.encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
+        EncodecFeatureExtractor,
     )
     from .models.encoder_decoder import EncoderDecoderConfig
     from .models.ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig
@@ -4265,7 +4265,7 @@ if TYPE_CHECKING:
     from .models.mask2former import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Mask2FormerConfig
     from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig, MaskFormerSwinConfig
     from .models.mbart import MBartConfig
-    from .models.mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTProcessor
+    from .models.mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTFeatureExtractor, MCTCTProcessor
     from .models.mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig
     from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
     from .models.mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig, MgpstrProcessor, MgpstrTokenizer
@@ -4355,6 +4355,7 @@ if TYPE_CHECKING:
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechT5Config,
+        SpeechT5FeatureExtractor,
         SpeechT5HifiGanConfig,
         SpeechT5Processor,
     )
@@ -4386,7 +4387,7 @@ if TYPE_CHECKING:
         TransfoXLTokenizer,
     )
     from .models.trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig, TrOCRProcessor
-    from .models.tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig, TvltProcessor
+    from .models.tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig, TvltFeatureExtractor, TvltProcessor
     from .models.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
     from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig
     from .models.upernet import UperNetConfig
@@ -4681,11 +4682,7 @@ if TYPE_CHECKING:
         from .utils.dummy_speech_objects import *
     else:
         from .models.audio_spectrogram_transformer import ASTFeatureExtractor
-        from .models.encodec import EncodecFeatureExtractor
-        from .models.mctct import MCTCTFeatureExtractor
         from .models.speech_to_text import Speech2TextFeatureExtractor
-        from .models.speecht5 import SpeechT5FeatureExtractor
-        from .models.tvlt import TvltFeatureExtractor
 
     try:
         if not is_tensorflow_text_available():

src/transformers/models/mctct/__init__.py

@@ -13,24 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
+    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
     "processing_mctct": ["MCTCTProcessor"],
 }
 
 
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_mctct"] = ["MCTCTFeatureExtractor"]
-
-
 try:
     if not is_torch_available():
         raise OptionalDependencyNotAvailable()
@@ -47,16 +39,9 @@ else:
 
 if TYPE_CHECKING:
     from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
+    from .feature_extraction_mctct import MCTCTFeatureExtractor
     from .processing_mctct import MCTCTProcessor
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_mctct import MCTCTFeatureExtractor
-
     try:
         if not is_torch_available():
             raise OptionalDependencyNotAvailable()

src/transformers/models/mctct/feature_extraction_mctct.py

@@ -19,9 +19,8 @@ Feature extractor class for M-CTC-T
 from typing import List, Optional, Union
 
 import numpy as np
-import torch
 
-from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram
+from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
 from ...file_utils import PaddingStrategy, TensorType
@@ -110,11 +109,9 @@ class MCTCTFeatureExtractor(SequenceFeatureExtractor):
         Extracts MFSC Features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC code.
         """
         if self.win_function == "hamming_window":
-            window = torch.hamming_window(window_length=self.sample_size, periodic=False, alpha=0.54, beta=0.46)
+            window = window_function(window_length=self.sample_size, name=self.win_function, periodic=False)
         else:
-            window = getattr(torch, self.win_function)()
-
-        window = window.numpy()
+            window = window_function(window_length=self.sample_size, name=self.win_function)
 
         fbanks = mel_filter_bank(
             num_frequency_bins=self.n_freqs,

src/transformers/models/speecht5/__init__.py

@@ -17,7 +17,6 @@ from ...utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_sentencepiece_available,
-    is_speech_available,
     is_torch_available,
 )
 
@@ -29,6 +28,7 @@ _import_structure = {
         "SpeechT5Config",
         "SpeechT5HifiGanConfig",
     ],
+    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
     "processing_speecht5": ["SpeechT5Processor"],
 }
 
@@ -40,14 +40,6 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["tokenization_speecht5"] = ["SpeechT5Tokenizer"]
 
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_speecht5"] = ["SpeechT5FeatureExtractor"]
-
 try:
     if not is_torch_available():
         raise OptionalDependencyNotAvailable()
@@ -71,6 +63,7 @@ if TYPE_CHECKING:
         SpeechT5Config,
         SpeechT5HifiGanConfig,
     )
+    from .feature_extraction_speecht5 import SpeechT5FeatureExtractor
     from .processing_speecht5 import SpeechT5Processor
 
     try:
@@ -81,14 +74,6 @@ if TYPE_CHECKING:
     else:
         from .tokenization_speecht5 import SpeechT5Tokenizer
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_speecht5 import SpeechT5FeatureExtractor
-
     try:
         if not is_torch_available():
             raise OptionalDependencyNotAvailable()

src/transformers/models/speecht5/feature_extraction_speecht5.py

@@ -18,9 +18,8 @@ import warnings
 from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
-import torch
 
-from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram
+from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
 from ...utils import PaddingStrategy, TensorType, logging
@@ -113,8 +112,7 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
         self.n_fft = optimal_fft_length(self.sample_size)
         self.n_freqs = (self.n_fft // 2) + 1
 
-        window = getattr(torch, self.win_function)(window_length=self.sample_size, periodic=True)
-        self.window = window.numpy().astype(np.float64)
+        self.window = window_function(window_length=self.sample_size, name=self.win_function, periodic=True)
 
         self.mel_filters = mel_filter_bank(
             num_frequency_bins=self.n_freqs,

src/transformers/models/tvlt/__init__.py

@@ -20,7 +20,6 @@ from typing import TYPE_CHECKING
 from ...utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
-    is_speech_available,
     is_torch_available,
     is_vision_available,
 )
@@ -28,6 +27,7 @@ from ...utils import (
 
 _import_structure = {
     "configuration_tvlt": ["TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig"],
+    "feature_extraction_tvlt": ["TvltFeatureExtractor"],
     "processing_tvlt": ["TvltProcessor"],
 }
 
@@ -53,17 +53,11 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["image_processing_tvlt"] = ["TvltImageProcessor"]
 
-try:
-    if not is_speech_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_tvlt"] = ["TvltFeatureExtractor"]
 
 if TYPE_CHECKING:
     from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
     from .processing_tvlt import TvltProcessor
+    from .feature_extraction_tvlt import TvltFeatureExtractor
 
     try:
         if not is_torch_available():
@@ -87,13 +81,6 @@ if TYPE_CHECKING:
     else:
         from .image_processing_tvlt import TvltImageProcessor
 
-    try:
-        if not is_speech_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_tvlt import TvltFeatureExtractor
 
 else:
     import sys

src/transformers/utils/dummy_speech_objects.py

@@ -9,36 +9,8 @@ class ASTFeatureExtractor(metaclass=DummyObject):
         requires_backends(self, ["speech"])
 
 
-class EncodecFeatureExtractor(metaclass=DummyObject):
-    _backends = ["speech"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["speech"])
-
-
-class MCTCTFeatureExtractor(metaclass=DummyObject):
-    _backends = ["speech"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["speech"])
-
-
 class Speech2TextFeatureExtractor(metaclass=DummyObject):
     _backends = ["speech"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["speech"])
-
-
-class SpeechT5FeatureExtractor(metaclass=DummyObject):
-    _backends = ["speech"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["speech"])
-
-
-class TvltFeatureExtractor(metaclass=DummyObject):
-    _backends = ["speech"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["speech"])

tests/models/encodec/test_feature_extraction_encodec.py

@@ -20,16 +20,13 @@ import unittest
 
 import numpy as np
 
-from transformers import is_speech_available
+from transformers import EncodecFeatureExtractor
 from transformers.testing_utils import require_torch
 from transformers.utils.import_utils import is_torch_available
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
 
 
-if is_speech_available():
-    from transformers import EncodecFeatureExtractor
-
 if is_torch_available():
     import torch
 
@@ -103,7 +100,7 @@ class EnCodecFeatureExtractionTester(unittest.TestCase):
 
 @require_torch
 class EnCodecFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
-    feature_extraction_class = EncodecFeatureExtractor if is_speech_available() else None
+    feature_extraction_class = EncodecFeatureExtractor
 
     def setUp(self):
         self.feat_extract_tester = EnCodecFeatureExtractionTester(self)

tests/models/mctct/test_feature_extraction_mctct.py

@@ -20,15 +20,12 @@ import unittest
 
 import numpy as np
 
-from transformers import is_speech_available
+from transformers import MCTCTFeatureExtractor
 from transformers.testing_utils import require_torch
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
 
 
-if is_speech_available():
-    from transformers import MCTCTFeatureExtractor
-
 global_rng = random.Random()
 
 
@@ -102,7 +99,7 @@ class MCTCTFeatureExtractionTester(unittest.TestCase):
 
 @require_torch
 class MCTCTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
-    feature_extraction_class = MCTCTFeatureExtractor if is_speech_available() else None
+    feature_extraction_class = MCTCTFeatureExtractor
 
     def setUp(self):
         self.feat_extract_tester = MCTCTFeatureExtractionTester(self)

tests/models/speecht5/test_feature_extraction_speecht5.py

@@ -20,16 +20,13 @@ import unittest
 
 import numpy as np
 
-from transformers import BatchFeature, is_speech_available
+from transformers import BatchFeature, SpeechT5FeatureExtractor
 from transformers.testing_utils import require_torch
 from transformers.utils.import_utils import is_torch_available
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
 
 
-if is_speech_available():
-    from transformers import SpeechT5FeatureExtractor
-
 if is_torch_available():
     import torch
 
@@ -142,7 +139,7 @@ class SpeechT5FeatureExtractionTester(unittest.TestCase):
 
 @require_torch
 class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
-    feature_extraction_class = SpeechT5FeatureExtractor if is_speech_available() else None
+    feature_extraction_class = SpeechT5FeatureExtractor
 
     def setUp(self):
         self.feat_extract_tester = SpeechT5FeatureExtractionTester(self)

tests/models/tvlt/test_feature_extraction_tvlt.py

@@ -22,7 +22,7 @@ import unittest
 
 import numpy as np
 
-from transformers import is_datasets_available, is_speech_available
+from transformers import TvltFeatureExtractor, is_datasets_available
 from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
 from transformers.utils.import_utils import is_torch_available
 
@@ -35,9 +35,6 @@ if is_torch_available():
 if is_datasets_available():
     from datasets import load_dataset
 
-if is_speech_available():
-    from transformers import TvltFeatureExtractor
-
 global_rng = random.Random()
 
 
@@ -111,7 +108,7 @@ class TvltFeatureExtractionTester(unittest.TestCase):
 @require_torch
 @require_torchaudio
 class TvltFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
-    feature_extraction_class = TvltFeatureExtractor if is_speech_available() else None
+    feature_extraction_class = TvltFeatureExtractor
 
     def setUp(self):
         self.feat_extract_tester = TvltFeatureExtractionTester(self)