SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add numpy alternative to FE using torchaudio (#26339)

Browse Source 
* 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
This commit is contained in:
Yoach Lacombe
2023-11-08 07:39:37 +00:00
committed by GitHub
parent e264745051
commit be74b2ead6
9 changed files with 208 additions and 98 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py
View File

@@ -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())