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Fix: [SeamlessM4T - S2TT] Bug in batch loading of audio in torch.Tensor format in the SeamlessM4TFeatureExtractor class (#27914)

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* fixes: code fixes on is_batched condition to also check for batched audio data in torch.Tensor format instead of only just checking for batched audio data in np.ndarray format

* Update src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py

Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>

* refactor: code refactoring to remove torch framework dependency

* docs: updated docstring to add torch tensor compatibility

* test: add test cases to incorporate torch tensor inputs

* test: ran make fix-copies for code conformity

* test: refactor test to separate the test_call into test_call_numpy and test_call_torch

---------

Co-authored-by: Yoach Lacombe <52246514+ylacombe@users.noreply.github.com>
This commit is contained in:
Nicholas Neo
2023-12-22 18:47:30 +08:00
committed by GitHub
parent 548a8f6119
commit 1ef86c4f56
2 changed files with 56 additions and 9 deletions
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37
tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
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@@ -139,7 +139,7 @@ class SeamlessM4TFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
dict_second = feat_extract_second.to_dict()
self.assertEqual(dict_first, dict_second)
def test_call(self):
def test_call_numpy(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
@@ -171,6 +171,41 @@ 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))
@require_torch
def test_call_torch(self):
import torch
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
pt_speech_inputs = [torch.tensor(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(pt_speech_inputs, padding=True, return_tensors="pt").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)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs[0], return_tensors="pt").input_features
self.assertTrue(torch.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(torch.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)]
pt_speech_inputs = torch.tensor(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="pt").input_features
encoded_sequences_2 = feature_extractor(pt_speech_inputs, return_tensors="pt").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(torch.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
@require_torch
# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad
def test_double_precision_pad(self):