fix: SeamlessM4TFeatureExtractor stride remainder (#32088)

* fix: SeamlessM4TFeatureExtractor stride remainder

* Added attention mask size test

* Reran ruff for style correction

src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py

@@ -286,14 +286,14 @@ class SeamlessM4TFeatureExtractor(SequenceFeatureExtractor):
 
         remainder = num_frames % self.stride
         if remainder != 0:
-            input_features = input_features[:, :num_frames, :]
-            attention_mask = attention_mask[:, :num_frames]
+            input_features = input_features[:, : num_frames - remainder, :]
+            attention_mask = attention_mask[:, : num_frames - remainder]
 
         input_features = np.reshape(
             input_features, (batch_size, num_frames // self.stride, num_channels * self.stride)
         )
 
-        indices = np.arange(0, num_frames)
+        indices = np.arange(0, num_frames - remainder)
         attention_mask = attention_mask[:, indices % self.stride == 1]
 
         padded_inputs["input_features"] = input_features

tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py

@@ -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)