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fix: SeamlessM4TFeatureExtractor stride remainder (#32088)

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* fix: SeamlessM4TFeatureExtractor stride remainder

* Added attention mask size test

* Reran ruff for style correction
This commit is contained in:
TechInterMezzo
2024-08-05 08:40:58 +02:00
committed by GitHub
parent 847bb856d5
commit 05ae3a300d
2 changed files with 60 additions and 3 deletions
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57
tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
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@@ -171,6 +171,63 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_call_with_padded_input_not_multiple_of_stride(self):
# same as test_call_numpy but with stride=6 and pad_to_multiple_of=8
# the input sizes 800, 1400 and 200 are a multiple of pad_to_multiple_of but not a multiple of stride
# therefore remainder = num_frames % self.stride will not be zero and must be subtracted from num_frames
stride = 6
pad_to_multiple_of = 8
feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict()
feature_extractor_args["stride"] = stride
feature_extractor = self.feature_extraction_class(**feature_extractor_args)
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size and attention mask size
output = feature_extractor(np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np")
input_features = output.input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[0] == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size * feature_extractor.stride)
# same as test_attention_mask
attention_mask = output.attention_mask
self.assertTrue(attention_mask.ndim == 2)
self.assertTrue(attention_mask.shape[0] == 3)
self.assertTrue(attention_mask.shape[-1] == input_features.shape[1])
# Test not batched input
encoded_sequences_1 = feature_extractor(
speech_inputs[0], pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
encoded_sequences_2 = feature_extractor(
np_speech_inputs[0], pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(
speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
encoded_sequences_2 = feature_extractor(
np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(
speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
encoded_sequences_2 = feature_extractor(
np_speech_inputs, pad_to_multiple_of=pad_to_multiple_of, return_tensors="np"
).input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_call_without_attention_mask(self):
feature_extractor_args = self.feat_extract_tester.prepare_feat_extract_dict()
feature_extractor = self.feature_extraction_class(**feature_extractor_args)