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correct (#13585)

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This commit is contained in:
Patrick von Platen
2021-09-16 09:07:20 +02:00
committed by GitHub
parent 89da1bfeac
commit b5bab710f7
1 changed files with 14 additions and 6 deletions
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20
tests/test_feature_extraction_speech_to_text.py
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@@ -137,11 +137,15 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
var_tolerances = [1e-3, 1e-3, 5e-1]
# TODO(Patrick, Suraj, Anton) - It's surprising that "non-padded/non-numpified" padding
# results in quite inaccurate variance computation after (see 5e-1 tolerance)
# Issue is filed and PR is underway: https://github.com/huggingface/transformers/issues/13539
# paddings = ["longest", "max_length", "do_not_pad"]
# max_lengths = [None, 16, None]
# var_tolerances = [1e-3, 1e-3, 5e-1]
paddings = ["longest", "max_length"]
max_lengths = [None, 16]
var_tolerances = [1e-3, 1e-3]
for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
inputs = feature_extractor(
@@ -163,11 +167,15 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 16, None]
var_tolerances = [1e-3, 1e-3, 5e-1]
# TODO(Patrick, Suraj, Anton) - It's surprising that "non-padded/non-numpified" padding
# results in quite inaccurate variance computation after (see 5e-1 tolerance)
# Issue is filed and PR is underway: https://github.com/huggingface/transformers/issues/13539
# paddings = ["longest", "max_length", "do_not_pad"]
# max_lengths = [None, 16, None]
# var_tolerances = [1e-3, 1e-3, 5e-1]
paddings = ["longest", "max_length"]
max_lengths = [None, 16]
var_tolerances = [1e-3, 1e-3]
for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
inputs = feature_extractor(
speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True