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

* 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>

src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py

@@ -20,6 +20,12 @@ from typing import List, Optional, Union
 
 import numpy as np
 
+from ...utils import is_torch_available
+
+
+if is_torch_available():
+    import torch
+
 from ...audio_utils import mel_filter_bank, spectrogram, window_function
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
@@ -152,14 +158,17 @@ class SeamlessM4TFeatureExtractor(SequenceFeatureExtractor):
         Main method to featurize and prepare for the model one or several sequence(s).
 
         Args:
-            raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`, `List[List[List[float]]]`):
+            raw_speech (`np.ndarray`, `torch.Tensor`, `List[float]`, `List[np.ndarray]`, `List[torch.Tensor]`,
-                The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
+            `List[List[float]]`, `List[List[List[float]]]`):
-                values, a list of numpy arrays, a list of list of float values or a list of a list of list of float
+                The sequence or batch of sequences to be padded. Each sequence can be a numpy array,
-                values. If `raw_speech` is a one-dimensional `np.ndarray` or a `List[float]`, `raw_speech` is
+                a torch tensor, a list of float values, a list of numpy arrays, a list of torch tensors,
+                a list of list of float values or a list of a list of list of float values.
+                If `raw_speech` is a one-dimensional `np.ndarray`, `torch.Tensor` or a `List[float]`, `raw_speech` is
                 considered a single-channel, single-sample sound. In all other cases, the first dimension of
-                `raw_speech`, whether from an `np.ndarray` or a `List[...]`, corresponds to the number of samples in
+                `raw_speech`, whether from an `np.ndarray`, a `torch.Tensor` or a `List[...]`,
-                the batch, and the number of channels (i.e. mono or stereo character) is derived from the other
+                corresponds to the number of samples in the batch, and the number of channels
-                dimensions (1D -> single-channel waveform batches; 2D-> stereo-channel waveform batches).
+                (i.e. mono or stereo character) is derived from the other dimensions
+                (1D -> single-channel waveform batches; 2D-> stereo-channel waveform batches).
             padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:
@@ -224,8 +233,11 @@ class SeamlessM4TFeatureExtractor(SequenceFeatureExtractor):
         if is_batched_numpy and len(raw_speech.shape) > 3:
             raise ValueError(f"Only mono-channel or stereo-channel audio is supported for input to {self}")
 
+        acceptable_types = (
+            (torch.Tensor, np.ndarray, tuple, list) if is_torch_available() else (np.ndarray, tuple, list)
+        )
         is_batched = is_batched_numpy or (
-            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], acceptable_types))
         )
 
         if is_batched:

tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py

@@ -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):