Add numpy alternative to FE using torchaudio (#26339)

* add audio_utils usage in the FE of SpeechToText

* clean unecessary parameters of AudioSpectrogramTransformer FE

* add audio_utils usage in AST

* add serialization tests and function to FEs

* make style

* remove use_torchaudio and move to_dict to FE

* test audio_utils usage

* make style and fix import (remove torchaudio dependency import)

* fix torch dependency for jax and tensor tests

* fix typo

* clean tests with suggestions

* add lines to test if is_speech_availble is False

tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py

@@ -15,13 +15,15 @@
 
 
 import itertools
+import os
 import random
+import tempfile
 import unittest
 
 import numpy as np
 
 from transformers import ASTFeatureExtractor
-from transformers.testing_utils import require_torch, require_torchaudio
+from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
 from transformers.utils.import_utils import is_torch_available
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
@@ -173,3 +175,48 @@ class ASTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Test
         input_values = feature_extractor(input_speech, return_tensors="pt").input_values
         self.assertEquals(input_values.shape, (1, 1024, 128))
         self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))
+
+    def test_feat_extract_from_and_save_pretrained(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertDictEqual(dict_first, dict_second)
+
+    def test_feat_extract_to_json_file(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
+            feat_extract_first.to_json_file(json_file_path)
+            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertEqual(dict_first, dict_second)
+
+
+# exact same tests than before, except that we simulate that torchaudio is not available
+@require_torch
+@unittest.mock.patch(
+    "transformers.models.audio_spectrogram_transformer.feature_extraction_audio_spectrogram_transformer.is_speech_available",
+    lambda: False,
+)
+class ASTFeatureExtractionWithoutTorchaudioTest(ASTFeatureExtractionTest):
+    def test_using_audio_utils(self):
+        # Tests that it uses audio_utils instead of torchaudio
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+
+        self.assertTrue(hasattr(feat_extract, "window"))
+        self.assertTrue(hasattr(feat_extract, "mel_filters"))
+
+        from transformers.models.audio_spectrogram_transformer.feature_extraction_audio_spectrogram_transformer import (
+            is_speech_available,
+        )
+
+        self.assertFalse(is_speech_available())

tests/models/speech_to_text/test_feature_extraction_speech_to_text.py

@@ -15,20 +15,19 @@
 
 
 import itertools
+import os
 import random
+import tempfile
 import unittest
 
 import numpy as np
 
-from transformers import is_speech_available
-from transformers.testing_utils import require_torch, require_torchaudio
+from transformers import Speech2TextFeatureExtractor
+from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
 
 
-if is_speech_available():
-    from transformers import Speech2TextFeatureExtractor
-
 global_rng = random.Random()
 
 
@@ -105,7 +104,7 @@ class Speech2TextFeatureExtractionTester(unittest.TestCase):
 @require_torch
 @require_torchaudio
 class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
-    feature_extraction_class = Speech2TextFeatureExtractor if is_speech_available() else None
+    feature_extraction_class = Speech2TextFeatureExtractor
 
     def setUp(self):
         self.feat_extract_tester = Speech2TextFeatureExtractionTester(self)
@@ -280,3 +279,45 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
         input_features = feature_extractor(input_speech, return_tensors="pt").input_features
         self.assertEquals(input_features.shape, (1, 584, 24))
         self.assertTrue(np.allclose(input_features[0, 0, :30], expected, atol=1e-4))
+
+    def test_feat_extract_from_and_save_pretrained(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertDictEqual(dict_first, dict_second)
+
+    def test_feat_extract_to_json_file(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
+            feat_extract_first.to_json_file(json_file_path)
+            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        self.assertEqual(dict_first, dict_second)
+
+
+# exact same tests than before, except that we simulate that torchaudio is not available
+@require_torch
+@unittest.mock.patch(
+    "transformers.models.speech_to_text.feature_extraction_speech_to_text.is_speech_available", lambda: False
+)
+class Speech2TextFeatureExtractionWithoutTorchaudioTest(Speech2TextFeatureExtractionTest):
+    def test_using_audio_utils(self):
+        # Tests that it uses audio_utils instead of torchaudio
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+
+        self.assertTrue(hasattr(feat_extract, "window"))
+        self.assertTrue(hasattr(feat_extract, "mel_filters"))
+
+        from transformers.models.speech_to_text.feature_extraction_speech_to_text import is_speech_available
+
+        self.assertFalse(is_speech_available())

tests/models/speech_to_text/test_processor_speech_to_text.py

@@ -18,7 +18,7 @@ import unittest
 from pathlib import Path
 from shutil import copyfile
 
-from transformers import Speech2TextTokenizer, is_speech_available
+from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor, Speech2TextTokenizer
 from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
 from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
 from transformers.utils import FEATURE_EXTRACTOR_NAME
@@ -26,10 +26,6 @@ from transformers.utils import FEATURE_EXTRACTOR_NAME
 from .test_feature_extraction_speech_to_text import floats_list
 
 
-if is_speech_available():
-    from transformers import Speech2TextFeatureExtractor, Speech2TextProcessor
-
-
 SAMPLE_SP = get_tests_dir("fixtures/test_sentencepiece.model")