is_batched fix for remaining 2-D numpy arrays (huggingface#23309)

* Fix is_batched code to allow 2-D numpy arrays for audio

* Tests

* Fix typo

* Incorporate comments from PR huggingface#23223

src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py

@@ -135,7 +135,8 @@ def __call__(
         Args:
             raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values.
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
             sampling_rate (`int`, *optional*):
                 The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
                 `sampling_rate` at the forward call to prevent silent errors.
@@ -160,9 +161,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

src/transformers/models/clap/feature_extraction_clap.py

@@ -272,7 +272,8 @@ def __call__(
         Args:
             raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values.
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
             truncation (`str`, *optional*):
                 Truncation pattern for long audio inputs. Two patterns are available:
                     - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and
@@ -312,9 +313,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

src/transformers/models/mctct/feature_extraction_mctct.py

@@ -180,7 +180,8 @@ def __call__(
         Args:
             raw_speech (`torch.Tensor`, `np.ndarray`, `List[float]`, `List[torch.Tensor]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a tensor, a numpy array, a list
-                of float values, a list of tensors, a list of numpy arrays or a list of list of float values.
+                of float values, a list of tensors, a list of numpy arrays or a list of list of float values. Must be
+                mono channel audio, not stereo, i.e. single float per timestep.
             padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:
@@ -231,9 +232,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py

@@ -141,7 +141,8 @@ def __call__(
         Args:
             raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values.
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
             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:
@@ -200,9 +201,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

src/transformers/models/speecht5/feature_extraction_speecht5.py

@@ -201,7 +201,8 @@ def __call__(
         Args:
             audio (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`, *optional*):
                 The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values. This outputs waveform features.
+                values, a list of numpy arrays or a list of list of float values. This outputs waveform features. Must
+                be mono channel audio, not stereo, i.e. single float per timestep.
             audio_target (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`, *optional*):
                 The sequence or batch of sequences to be processed as targets. Each sequence can be a numpy array, a
                 list of float values, a list of numpy arrays or a list of list of float values. This outputs log-mel
@@ -307,9 +308,11 @@ def _process_audio(
         return_tensors: Optional[Union[str, TensorType]] = None,
         **kwargs,
     ) -> BatchFeature:
-        is_batched = bool(
-            isinstance(speech, (list, tuple))
-            and (isinstance(speech[0], np.ndarray) or isinstance(speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(speech, np.ndarray) and len(speech.shape) > 1
+        if is_batched_numpy and len(speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(speech, (list, tuple)) and (isinstance(speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

src/transformers/models/tvlt/feature_extraction_tvlt.py

@@ -129,7 +129,8 @@ def __call__(
         Args:
             raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values.
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors instead of list of python integers. Acceptable values are:
                 - `'pt'`: Return PyTorch `torch.Tensor` objects.
@@ -176,9 +177,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
         if is_batched:
             raw_speech = [np.asarray([speech], dtype=np.float32).T for speech in raw_speech]

src/transformers/models/whisper/feature_extraction_whisper.py

@@ -152,7 +152,8 @@ def __call__(
         Args:
             raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                 The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
-                values, a list of numpy arrays or a list of list of float values.
+                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
+                stereo, i.e. single float per timestep.
             truncation (`bool`, *optional*, default to `True`):
                 Activates truncation to cut input sequences longer than *max_length* to *max_length*.
             pad_to_multiple_of (`int`, *optional*, defaults to None):
@@ -203,9 +204,11 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
-        is_batched = bool(
-            isinstance(raw_speech, (list, tuple))
-            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
         )
 
         if is_batched:

tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py

@@ -125,6 +125,14 @@ def test_call(self):
         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 = feat_extract(speech_inputs, return_tensors="np").input_values
+        encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
+        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_double_precision_pad(self):
         import torch

tests/models/clap/test_feature_extraction_clap.py

@@ -139,6 +139,14 @@ def test_call(self):
         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, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, 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_double_precision_pad(self):
         import torch
 

tests/models/mctct/test_feature_extraction_mctct.py

@@ -134,6 +134,14 @@ def test_call(self):
         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, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, 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_cepstral_mean_and_variance_normalization(self):
         feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]

tests/models/speech_to_text/test_feature_extraction_speech_to_text.py

@@ -136,6 +136,14 @@ def test_call(self):
         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, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, 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_cepstral_mean_and_variance_normalization(self):
         feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]

tests/models/speecht5/test_feature_extraction_speecht5.py

@@ -275,6 +275,14 @@ def test_call_target(self):
         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, return_tensors="np").input_values
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_values
+        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_batch_feature_target(self):
         speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
         feat_extract = self.feature_extraction_class(**self.feat_extract_dict)

tests/models/tvlt/test_feature_extraction_tvlt.py

@@ -189,6 +189,15 @@ def test_call(self):
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
 
+        # 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_audios = feature_extractor(np_speech_inputs, return_tensors="np", sampling_rate=44100).audio_values
+        self.assertTrue(encoded_audios.ndim == 4)
+        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
+        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
+        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
+
     def _load_datasamples(self, num_samples):
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech

tests/models/whisper/test_feature_extraction_whisper.py

@@ -173,6 +173,14 @@ def test_call(self):
         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, return_tensors="np").input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, 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 truncation required
         speech_inputs = [floats_list((1, x))[0] for x in range(200, (feature_extractor.n_samples + 500), 200)]
         np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]