Repository: huggingface/transformers
Pull request: Fix gemma3n feature extractor's incorrect squeeze

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Fixes #39911
- Only squeeze batch_size dimension for `mel_spectrogram` in gemma3n processor

TODO:
- [ ] Add test


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cc @NickLucche Can you check if this fix works on your gemma3n PR? Thanks!

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Changed files:
filename: src/transformers/models/gemma3n/feature_extraction_gemma3n.py
patch: @@ -261,7 +261,7 @@ def _extract_spectrogram(self, waveform: np.ndarray, attention_mask: np.ndarray)
         if self.per_bin_stddev is not None:
             log_mel_spec = log_mel_spec / self.per_bin_stddev  # Broadcasting
 
-        mel_spectrogram = log_mel_spec.squeeze()
+        mel_spectrogram = log_mel_spec.squeeze(0)
         mask = attention_mask[:: self.hop_length].astype(bool)
         # TODO: The filtered mask is always exactly 3 elements longer than the mel_spectrogram. Why???
         return mel_spectrogram, mask[: mel_spectrogram.shape[0]]

filename: tests/models/gemma3n/test_feature_extraction_gemma3n.py
patch: @@ -228,6 +228,25 @@ def test_call(self, audio_inputs, test_truncation=False):
             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_audio_features_attn_mask_consistent(self):
+        # regression test for https://github.com/huggingface/transformers/issues/39911
+        # Test input_features and input_features_mask have consistent shape
+        np.random.seed(42)
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        for i in [512, 640, 1024]:
+            audio = np.random.randn(i)
+            mm_data = {
+                "raw_speech": [audio],
+                "sampling_rate": 16000,
+            }
+            inputs = feature_extractor(**mm_data, return_tensors="np")
+            out = inputs["input_features"]
+            mask = inputs["input_features_mask"]
+
+            assert out.ndim == 3
+            assert mask.ndim == 2
+            assert out.shape[:2] == mask.shape[:2]
+
     def test_dither(self):
         np.random.seed(42)  # seed the dithering randn()