[Sequence Feature Extraction] Add truncation (#12804)

* fix_torch_device_generate_test

* remove @

* add truncate

* finish

* correct test

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* clean tests

* correct normalization for truncation

* remove casting

* up

* save intermed

* finish

* finish

* correct

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/feature_extraction_sequence_utils.py

@@ -69,6 +69,7 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
         ],
         padding: Union[bool, str, PaddingStrategy] = True,
         max_length: Optional[int] = None,
+        truncation: bool = False,
         pad_to_multiple_of: Optional[int] = None,
         return_attention_mask: Optional[bool] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
@@ -107,6 +108,8 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
                   different lengths).
             max_length (:obj:`int`, `optional`):
                 Maximum length of the returned list and optionally padding length (see above).
+            truncation (:obj:`bool`):
+                Activates truncation to cut input sequences longer than :obj:`max_length` to :obj:`max_length`.
             pad_to_multiple_of (:obj:`int`, `optional`):
                 If set will pad the sequence to a multiple of the provided value.
 
@@ -178,10 +181,18 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
                 processed_features[key] = to_py_obj(value)
 
         # Convert padding_strategy in PaddingStrategy
-        padding_strategy, max_length, _ = self._get_padding_strategies(padding=padding, max_length=max_length)
+        padding_strategy = self._get_padding_strategies(padding=padding, max_length=max_length)
 
         required_input = processed_features[self.model_input_names[0]]
         if required_input and not isinstance(required_input[0], (list, tuple)):
+            # truncation
+            processed_features = self._truncate(
+                processed_features,
+                max_length=max_length,
+                pad_to_multiple_of=pad_to_multiple_of,
+                truncation=truncation,
+            )
+            # padding
             processed_features = self._pad(
                 processed_features,
                 max_length=max_length,
@@ -196,13 +207,32 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
             len(v) == batch_size for v in processed_features.values()
         ), "Some items in the output dictionary have a different batch size than others."
 
+        truncated_inputs = []
+        for i in range(batch_size):
+            inputs = dict((k, v[i]) for k, v in processed_features.items())
+            # truncation
+            inputs = self._truncate(
+                inputs,
+                max_length=max_length,
+                pad_to_multiple_of=pad_to_multiple_of,
+                truncation=truncation,
+            )
+            truncated_inputs.append(inputs)
+
         if padding_strategy == PaddingStrategy.LONGEST:
             max_length = max(len(inputs) for inputs in required_input)
             padding_strategy = PaddingStrategy.MAX_LENGTH
 
         batch_outputs = {}
         for i in range(batch_size):
-            inputs = dict((k, v[i]) for k, v in processed_features.items())
+            # truncation
+            inputs = self._truncate(
+                truncated_inputs[i],
+                max_length=max_length,
+                pad_to_multiple_of=pad_to_multiple_of,
+                truncation=truncation,
+            )
+            # padding
             outputs = self._pad(
                 inputs,
                 max_length=max_length,
@@ -278,6 +308,46 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
 
         return processed_features
 
+    def _truncate(
+        self,
+        processed_features: Union[Dict[str, List[float]], BatchFeature],
+        max_length: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+        truncation: Optional[bool] = None,
+    ):
+        """
+        Pad inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            processed_features: Dictionary of input values (`List[float]`) / input vectors (`List[List[float]]`) or batch of inputs values (`List[List[int]]`) / input vectors (`List[List[List[int]]]`)
+            max_length: maximum length of the returned list and optionally padding length (see below)
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
+            truncation: (optional) Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+        """
+        if not truncation:
+            return processed_features
+        elif truncation and max_length is None:
+            raise ValueError(
+                "When setting ``truncation=True``, make sure that ``max_length`` is defined and ``padding='max_length'``"
+            )
+
+        required_input = processed_features[self.model_input_names[0]]
+
+        # find `max_length` that fits `pad_to_multiple_of`
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_truncated = len(required_input) > max_length
+
+        if needs_to_be_truncated:
+            processed_features[self.model_input_names[0]] = processed_features[self.model_input_names[0]][:max_length]
+            if "attention_mask" in processed_features:
+                processed_features["attention_mask"] = processed_features["attention_mask"][:max_length]
+
+        return processed_features
+
     def _get_padding_strategies(self, padding=False, max_length=None, pad_to_multiple_of=None, **kwargs):
         """
         Find the correct padding strategy
@@ -308,4 +378,4 @@ class SequenceFeatureExtractor(FeatureExtractionMixin):
                 "Please select a value to use as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."
             )
 
-        return padding_strategy, max_length, kwargs
+        return padding_strategy

src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py

@@ -93,28 +93,37 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
 
     @staticmethod
     def utterance_cmvn(
-        x: np.ndarray, normalize_means: Optional[bool] = True, normalize_vars: Optional[bool] = True
+        x: np.ndarray, input_length: int, normalize_means: Optional[bool] = True, normalize_vars: Optional[bool] = True
     ) -> np.ndarray:
-        mean = x.mean(axis=0)
-        square_sums = (x ** 2).sum(axis=0)
+        # make sure we normalie float32 arrays
+
+        mean = x[:input_length].mean(axis=0)
+        square_sums = (x[:input_length] ** 2).sum(axis=0)
 
         if normalize_means:
             x = np.subtract(x, mean)
         if normalize_vars:
-            var = square_sums / x.shape[0] - mean ** 2
+            var = square_sums / x[:input_length].shape[0] - mean ** 2
             std = np.sqrt(np.maximum(var, 1e-10))
             x = np.divide(x, std)
 
+        # make sure array is in float32
+        x = x.astype(np.float32)
+
         return x
 
-    def normalize(self, input_values: List[np.ndarray]) -> List[np.ndarray]:
-        return [self.utterance_cmvn(x, self.normalize_means, self.normalize_vars) for x in input_values]
+    def normalize(self, input_values: List[np.ndarray], input_lengths: List[int]) -> List[np.ndarray]:
+        return [
+            self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars)
+            for x, n in zip(input_values, input_lengths)
+        ]
 
     def __call__(
         self,
         raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
         padding: Union[bool, str, PaddingStrategy] = False,
         max_length: Optional[int] = None,
+        truncation: bool = False,
         pad_to_multiple_of: Optional[int] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
         sampling_rate: Optional[int] = None,
@@ -140,6 +149,8 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
                   different lengths).
             max_length (:obj:`int`, `optional`):
                 Maximum length of the returned list and optionally padding length (see above).
+            truncation (:obj:`bool`):
+                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
             pad_to_multiple_of (:obj:`int`, `optional`):
                 If set will pad the sequence to a multiple of the provided value.
 
@@ -191,6 +202,8 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
             raw_speech = [np.asarray(speech) for speech in raw_speech]
         elif not is_batched and not isinstance(raw_speech, np.ndarray):
             raw_speech = np.asarray(raw_speech)
+        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.float64:
+            raw_speech = raw_speech.astype(np.float32)
 
         # always return batch
         if not is_batched:
@@ -199,10 +212,6 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
         # extract fbank features
         features = [self._extract_fbank_features(waveform) for waveform in raw_speech]
 
-        # Utterance-level cepstral mean and variance normalization
-        if self.do_ceptral_normalize:
-            features = self.normalize(features)
-
         # convert into correct format for padding
         encoded_inputs = BatchFeature({"input_features": features})
 
@@ -210,10 +219,29 @@ class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
             encoded_inputs,
             padding=padding,
             max_length=max_length,
+            truncation=truncation,
             pad_to_multiple_of=pad_to_multiple_of,
             return_attention_mask=return_attention_mask,
-            return_tensors=return_tensors,
             **kwargs,
         )
 
+        if "attention_mask" in padded_inputs:
+            input_lengths = padded_inputs["attention_mask"].sum(-1)
+        else:
+            padded_input_values = padded_inputs["input_features"]
+            input_lengths = [padded_input_values.shape[-1] for _ in range(padded_input_values.shape[0])]
+
+        # Utterance-level cepstral mean and variance normalization
+        if self.do_ceptral_normalize:
+            input_features = padded_inputs["input_features"]
+
+            # make sure list is in array format
+            if isinstance(input_features[0], list):
+                input_features = [np.asarray(feature, dtype=np.float32) for feature in input_features]
+
+            padded_inputs["input_features"] = self.normalize(input_features, input_lengths=input_lengths)
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
         return padded_inputs

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -758,7 +758,6 @@ class Speech2TextEncoder(Speech2TextPreTrainedModel):
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
         if attention_mask is not None:
             attention_mask = self._get_subsampled_encoder_attn_mask(attention_mask)
 

src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py

@@ -79,17 +79,24 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
         self.do_normalize = do_normalize
 
     @staticmethod
-    def zero_mean_unit_var_norm(input_values: List[np.ndarray]) -> List[np.ndarray]:
+    def zero_mean_unit_var_norm(input_values: List[np.ndarray], input_lengths: List[int]) -> List[np.ndarray]:
         """
         Every array in the list is normalized to have zero mean and unit variance
         """
-        return [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in input_values]
+        if isinstance(input_values[0], np.ndarray):
+            input_values = [x.astype(np.float32) for x in input_values]
+
+        normed_input_values = [
+            (x - np.mean(x[:i])) / np.sqrt(np.var(x[:i]) + 1e-5) for x, i in zip(input_values, input_lengths)
+        ]
+        return normed_input_values
 
     def __call__(
         self,
         raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
         padding: Union[bool, str, PaddingStrategy] = False,
         max_length: Optional[int] = None,
+        truncation: bool = False,
         pad_to_multiple_of: Optional[int] = None,
         return_attention_mask: Optional[bool] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
@@ -115,6 +122,8 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
                   different lengths).
             max_length (:obj:`int`, `optional`):
                 Maximum length of the returned list and optionally padding length (see above).
+            truncation (:obj:`bool`):
+                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
             pad_to_multiple_of (:obj:`int`, `optional`):
                 If set will pad the sequence to a multiple of the provided value.
 
@@ -168,18 +177,16 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
 
         # make sure input is in list format
         if is_batched and not isinstance(raw_speech[0], np.ndarray):
-            raw_speech = [np.asarray(speech) for speech in raw_speech]
+            raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech]
         elif not is_batched and not isinstance(raw_speech, np.ndarray):
-            raw_speech = np.asarray(raw_speech)
+            raw_speech = np.asarray(raw_speech, dtype=np.float32)
+        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.float64:
+            raw_speech = raw_speech.astype(np.float32)
 
         # always return batch
         if not is_batched:
             raw_speech = [raw_speech]
 
-        # zero-mean and unit-variance normalization
-        if self.do_normalize:
-            raw_speech = self.zero_mean_unit_var_norm(raw_speech)
-
         # convert into correct format for padding
         encoded_inputs = BatchFeature({"input_values": raw_speech})
 
@@ -187,9 +194,24 @@ class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
             encoded_inputs,
             padding=padding,
             max_length=max_length,
+            truncation=truncation,
             pad_to_multiple_of=pad_to_multiple_of,
             return_attention_mask=return_attention_mask,
-            return_tensors=return_tensors,
         )
 
+        if "attention_mask" in padded_inputs:
+            input_lengths = padded_inputs["attention_mask"].sum(-1)
+        else:
+            padded_input_values = padded_inputs["input_values"]
+            input_lengths = [padded_input_values.shape[-1] for _ in range(padded_input_values.shape[0])]
+
+        # zero-mean and unit-variance normalization
+        if self.do_normalize:
+            padded_inputs["input_values"] = self.zero_mean_unit_var_norm(
+                padded_inputs["input_values"], input_lengths=input_lengths
+            )
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
         return padded_inputs

tests/test_feature_extraction_speech_to_text.py

@@ -91,6 +91,7 @@ class Speech2TextFeatureExtractionTester(unittest.TestCase):
         if equal_length:
             speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
         else:
+            # make sure that inputs increase in size
             speech_inputs = [
                 floats_list((x, self.feature_size))
                 for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
@@ -147,3 +148,26 @@ class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unitt
         _check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
         _check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
         _check_zero_mean_unit_variance(input_features[2, : fbank_feat_lengths[2]])
+
+    def test_cepstral_mean_and_variance_normalization_trunc(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)]
+        inputs = feature_extractor(
+            speech_inputs,
+            padding="max_length",
+            max_length=4,
+            truncation=True,
+            return_tensors="np",
+            return_attention_mask=True,
+        )
+        input_features = inputs.input_features
+        attention_mask = inputs.attention_mask
+        fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
+
+        def _check_zero_mean_unit_variance(input_vector):
+            self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
+            self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
+
+        _check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
+        _check_zero_mean_unit_variance(input_features[1])
+        _check_zero_mean_unit_variance(input_features[2])

tests/test_feature_extraction_wav2vec2.py

@@ -83,6 +83,7 @@ class Wav2Vec2FeatureExtractionTester(unittest.TestCase):
         if equal_length:
             speech_inputs = floats_list((self.batch_size, self.max_seq_length))
         else:
+            # make sure that inputs increase in size
             speech_inputs = [
                 _flatten(floats_list((x, self.feature_size)))
                 for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
@@ -122,7 +123,7 @@ class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
     def test_zero_mean_unit_variance_normalization(self):
         feat_extract = 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)]
-        processed = feat_extract(speech_inputs, padding="longest")
+        processed = feat_extract(speech_inputs, padding="longest", return_tensors="np")
         input_values = processed.input_values
 
         def _check_zero_mean_unit_variance(input_vector):
@@ -133,6 +134,22 @@ class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
         _check_zero_mean_unit_variance(input_values[1, :1000])
         _check_zero_mean_unit_variance(input_values[2])
 
+    def test_zero_mean_unit_variance_normalization_trunc(self):
+        feat_extract = 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)]
+        processed = feat_extract(
+            speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
+        )
+        input_values = processed.input_values
+
+        def _check_zero_mean_unit_variance(input_vector):
+            self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
+            self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
+
+        _check_zero_mean_unit_variance(input_values[0, :800])
+        _check_zero_mean_unit_variance(input_values[1])
+        _check_zero_mean_unit_variance(input_values[2])
+
     @slow
     @require_torch
     def test_pretrained_checkpoints_are_set_correctly(self):

tests/test_modeling_wav2vec2.py

@@ -715,7 +715,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         input_speech = self._load_datasamples(2)
 
-        inputs = processor(input_speech, return_tensors="pt", padding=True, truncation=True)
+        inputs = processor(input_speech, return_tensors="pt", padding=True)
 
         input_values = inputs.input_values.to(torch_device)
 
@@ -737,7 +737,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
 
         input_speech = self._load_datasamples(4)
 
-        inputs = processor(input_speech, return_tensors="pt", padding=True, truncation=True)
+        inputs = processor(input_speech, return_tensors="pt", padding=True)
 
         input_values = inputs.input_values.to(torch_device)
         attention_mask = inputs.attention_mask.to(torch_device)

tests/test_sequence_feature_extraction_common.py

@@ -126,12 +126,17 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
         feature_size = self.feat_extract_tester.feature_size
 
         # test padding for List[int] + numpy
-        input_1 = feat_extract.pad(processed_features, padding=False)[input_name]
-        input_2 = feat_extract.pad(processed_features, padding="longest")[input_name]
-        input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))[
-            input_name
-        ]
-        input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
+        input_1 = feat_extract.pad(processed_features, padding=False)
+        input_1 = input_1[input_name]
+
+        input_2 = feat_extract.pad(processed_features, padding="longest")
+        input_2 = input_2[input_name]
+
+        input_3 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[-1]))
+        input_3 = input_3[input_name]
+
+        input_4 = feat_extract.pad(processed_features, padding="longest", return_tensors="np")
+        input_4 = input_4[input_name]
 
         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(ValueError):
@@ -139,7 +144,8 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
 
         input_5 = feat_extract.pad(
             processed_features, padding="max_length", max_length=pad_max_length, return_tensors="np"
-        )[input_name]
+        )
+        input_5 = input_5[input_name]
 
         self.assertFalse(_inputs_have_equal_length(input_1))
         self.assertTrue(_inputs_have_equal_length(input_2))
@@ -154,18 +160,25 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
             self.assertTrue(input_4.shape[2] == input_5.shape[2] == feature_size)
 
         # test padding for `pad_to_multiple_of` for List[int] + numpy
-        input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)[input_name]
-        input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)[input_name]
+        input_6 = feat_extract.pad(processed_features, pad_to_multiple_of=10)
+        input_6 = input_6[input_name]
+
+        input_7 = feat_extract.pad(processed_features, padding="longest", pad_to_multiple_of=10)
+        input_7 = input_7[input_name]
+
         input_8 = feat_extract.pad(
             processed_features, padding="max_length", pad_to_multiple_of=10, max_length=pad_max_length
-        )[input_name]
+        )
+        input_8 = input_8[input_name]
+
         input_9 = feat_extract.pad(
             processed_features,
             padding="max_length",
             pad_to_multiple_of=10,
             max_length=pad_max_length,
             return_tensors="np",
-        )[input_name]
+        )
+        input_9 = input_9[input_name]
 
         self.assertTrue(all(len(x) % 10 == 0 for x in input_6))
         self.assertTrue(_inputs_are_equal(input_6, input_7))
@@ -205,12 +218,149 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
             < 1e-3
         )
 
+    def _check_truncation(self, numpify=False):
+        def _inputs_have_equal_length(input):
+            length = len(input[0])
+            for input_slice in input[1:]:
+                if len(input_slice) != length:
+                    return False
+            return True
+
+        def _inputs_are_equal(input_1, input_2):
+            if len(input_1) != len(input_2):
+                return False
+
+            for input_slice_1, input_slice_2 in zip(input_1, input_2):
+                if not np.allclose(np.asarray(input_slice_1), np.asarray(input_slice_2), atol=1e-3):
+                    return False
+            return True
+
+        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
+        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common(numpify=numpify)
+        input_name = feat_extract.model_input_names[0]
+
+        processed_features = BatchFeature({input_name: speech_inputs})
+
+        # truncate to smallest
+        input_1 = feat_extract.pad(
+            processed_features, padding="max_length", max_length=len(speech_inputs[0]), truncation=True
+        )
+        input_1 = input_1[input_name]
+
+        input_2 = feat_extract.pad(processed_features, padding="max_length", max_length=len(speech_inputs[0]))
+        input_2 = input_2[input_name]
+
+        self.assertTrue(_inputs_have_equal_length(input_1))
+        self.assertFalse(_inputs_have_equal_length(input_2))
+
+        # truncate to smallest with np
+        input_3 = feat_extract.pad(
+            processed_features,
+            padding="max_length",
+            max_length=len(speech_inputs[0]),
+            return_tensors="np",
+            truncation=True,
+        )
+        input_3 = input_3[input_name]
+
+        input_4 = feat_extract.pad(
+            processed_features, padding="max_length", max_length=len(speech_inputs[0]), return_tensors="np"
+        )
+        input_4 = input_4[input_name]
+
+        self.assertTrue(_inputs_have_equal_length(input_3))
+        self.assertTrue(input_3.shape[1] == len(speech_inputs[0]))
+
+        # since truncation forces padding to be smaller than longest input
+        # function can't return `np.ndarray`, but has to return list
+        self.assertFalse(_inputs_have_equal_length(input_4))
+
+        # truncate to middle
+        input_5 = feat_extract.pad(
+            processed_features,
+            padding="max_length",
+            max_length=len(speech_inputs[1]),
+            truncation=True,
+            return_tensors="np",
+        )
+        input_5 = input_5[input_name]
+
+        input_6 = feat_extract.pad(
+            processed_features, padding="max_length", max_length=len(speech_inputs[1]), truncation=True
+        )
+        input_6 = input_6[input_name]
+
+        input_7 = feat_extract.pad(
+            processed_features, padding="max_length", max_length=len(speech_inputs[1]), return_tensors="np"
+        )
+        input_7 = input_7[input_name]
+
+        self.assertTrue(input_5.shape[1] == len(speech_inputs[1]))
+        self.assertTrue(_inputs_have_equal_length(input_5))
+        self.assertTrue(_inputs_have_equal_length(input_6))
+        self.assertTrue(_inputs_are_equal(input_5, input_6))
+
+        # since truncation forces padding to be smaller than longest input
+        # function can't return `np.ndarray`, but has to return list
+        self.assertFalse(_inputs_have_equal_length(input_7))
+        self.assertTrue(len(input_7[-1]) == len(speech_inputs[-1]))
+
+        # padding has to be max_length when setting `truncation=True`
+        with self.assertRaises(ValueError):
+            feat_extract.pad(processed_features, truncation=True)[input_name]
+
+        # padding has to be max_length when setting `truncation=True`
+        with self.assertRaises(ValueError):
+            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
+
+        # padding has to be max_length when setting `truncation=True`
+        with self.assertRaises(ValueError):
+            feat_extract.pad(processed_features, padding="longest", truncation=True)[input_name]
+
+        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
+        with self.assertRaises(ValueError):
+            feat_extract.pad(processed_features, padding="max_length", truncation=True)[input_name]
+
+        # test truncation for `pad_to_multiple_of` for List[int] + numpy
+        pad_to_multiple_of = 12
+        input_8 = feat_extract.pad(
+            processed_features,
+            padding="max_length",
+            max_length=len(speech_inputs[0]),
+            pad_to_multiple_of=pad_to_multiple_of,
+            truncation=True,
+        )
+        input_8 = input_8[input_name]
+
+        input_9 = feat_extract.pad(
+            processed_features,
+            padding="max_length",
+            max_length=len(speech_inputs[0]),
+            pad_to_multiple_of=pad_to_multiple_of,
+        )
+        input_9 = input_9[input_name]
+
+        # retrieve expected_length as multiple of pad_to_multiple_of
+        expected_length = len(speech_inputs[0])
+        if expected_length % pad_to_multiple_of != 0:
+            expected_length = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        self.assertTrue(len(input_8[0]) == expected_length)
+        self.assertTrue(_inputs_have_equal_length(input_8))
+        self.assertFalse(_inputs_have_equal_length(input_9))
+
     def test_padding_from_list(self):
         self._check_padding(numpify=False)
 
     def test_padding_from_array(self):
         self._check_padding(numpify=True)
 
+    def test_truncation_from_list(self):
+        self._check_truncation(numpify=False)
+
+    def test_truncation_from_array(self):
+        self._check_truncation(numpify=True)
+
     @require_torch
     def test_padding_accepts_tensors_pt(self):
         feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
@@ -251,3 +401,25 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
         self.assertIn("attention_mask", processed)
         self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
         self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
+
+    def test_attention_mask_with_truncation(self):
+        feat_dict = self.feat_extract_dict
+        feat_dict["return_attention_mask"] = True
+        feat_extract = self.feature_extraction_class(**feat_dict)
+        speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
+        input_lenghts = [len(x) for x in speech_inputs]
+        input_name = feat_extract.model_input_names[0]
+
+        processed = BatchFeature({input_name: speech_inputs})
+        max_length = min(input_lenghts)
+
+        processed_pad = feat_extract.pad(
+            processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
+        )
+        self.assertIn("attention_mask", processed_pad)
+        self.assertListEqual(
+            list(processed_pad.attention_mask.shape), list((processed_pad[input_name].shape[0], max_length))
+        )
+        self.assertListEqual(
+            processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
+        )