[Wav2Vec2] Fix normalization for non-padded tensors (#13512)

* finalize

* Apply suggestions from code review

* finish cleaner implementation

* more tests

* small fix

* finish

* up

src/transformers/feature_extraction_sequence_utils.py

@@ -341,7 +341,7 @@ def _truncate(
 
         return processed_features
 
-    def _get_padding_strategies(self, padding=False, max_length=None, pad_to_multiple_of=None, **kwargs):
+    def _get_padding_strategies(self, padding=False, max_length=None):
         """
         Find the correct padding strategy
         """

src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py

@@ -93,10 +93,13 @@ def _extract_fbank_features(
 
     @staticmethod
     def utterance_cmvn(
-        x: np.ndarray, input_length: int, 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,
+        padding_value: float = 0.0,
     ) -> np.ndarray:
         # make sure we normalie float32 arrays
-
         mean = x[:input_length].mean(axis=0)
         square_sums = (x[:input_length] ** 2).sum(axis=0)
 
@@ -107,15 +110,21 @@ def utterance_cmvn(
             std = np.sqrt(np.maximum(var, 1e-10))
             x = np.divide(x, std)
 
+        if x.shape[0] > input_length:
+            x[input_length:] = padding_value
+
         # make sure array is in float32
         x = x.astype(np.float32)
 
         return x
 
-    def normalize(self, input_values: List[np.ndarray], input_lengths: List[int]) -> List[np.ndarray]:
+    def normalize(
+        self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
+    ) -> List[np.ndarray]:
+        lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
-            self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars)
-            for x, n in zip(input_values, input_lengths)
+            self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars, self.padding_value)
+            for x, n in zip(input_features, lengths)
         ]
 
     def __call__(
@@ -197,7 +206,6 @@ def __call__(
             and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
         )
 
-        # 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]
         elif not is_batched and not isinstance(raw_speech, np.ndarray):
@@ -225,21 +233,25 @@ def __call__(
             **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])]
+        # make sure list is in array format
+        input_features = padded_inputs.get("input_features")
+        if isinstance(input_features[0], list):
+            padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]
+
+        attention_mask = padded_inputs.get("attention_mask")
+        if attention_mask is not None:
+            padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.bool) for array in attention_mask]
 
         # 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)
+            attention_mask = (
+                np.array(attention_mask, dtype=np.bool)
+                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
+                else None
+            )
+            padded_inputs["input_features"] = self.normalize(
+                padded_inputs["input_features"], attention_mask=attention_mask
+            )
 
         if return_tensors is not None:
             padded_inputs = padded_inputs.convert_to_tensors(return_tensors)

src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py

@@ -79,13 +79,25 @@ def __init__(
         self.do_normalize = do_normalize
 
     @staticmethod
-    def zero_mean_unit_var_norm(input_values: List[np.ndarray], input_lengths: List[int]) -> List[np.ndarray]:
+    def zero_mean_unit_var_norm(
+        input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0
+    ) -> List[np.ndarray]:
         """
         Every array in the list is normalized to have zero mean and unit variance
         """
-        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)
-        ]
+        if attention_mask is not None:
+            attention_mask = np.array(attention_mask, np.bool)
+            normed_input_values = []
+
+            for vector, length in zip(input_values, attention_mask.sum(-1)):
+                normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
+                if length > normed_slice.shape[0]:
+                    normed_slice[length:] = padding_value
+
+                normed_input_values.append(normed_slice)
+        else:
+            normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]
+
         return normed_input_values
 
     def __call__(
@@ -172,14 +184,6 @@ def __call__(
             and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
         )
 
-        # make sure input is in list format
-        if is_batched and not isinstance(raw_speech[0], np.ndarray):
-            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, 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]
@@ -196,19 +200,33 @@ def __call__(
             return_attention_mask=return_attention_mask,
         )
 
-        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])]
-
-        if isinstance(padded_inputs["input_values"][0], np.ndarray):
-            padded_inputs["input_values"] = [x.astype(np.float32) for x in padded_inputs["input_values"]]
+        # convert input values to correct format
+        input_values = padded_inputs["input_values"]
+        if not isinstance(input_values[0], np.ndarray):
+            padded_inputs["input_values"] = [np.asarray(array, dtype=np.float32) for array in input_values]
+        elif (
+            not isinstance(input_values, np.ndarray)
+            and isinstance(input_values[0], np.ndarray)
+            and input_values[0].dtype is np.float64
+        ):
+            padded_inputs["input_values"] = [array.astype(np.float32) for array in input_values]
+        elif isinstance(input_values, np.ndarray) and input_values.dtype is np.float64:
+            padded_inputs["input_values"] = input_values.astype(np.float32)
+
+        # convert attention_mask to correct format
+        attention_mask = padded_inputs.get("attention_mask")
+        if attention_mask is not None:
+            padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.bool) for array in attention_mask]
 
         # zero-mean and unit-variance normalization
         if self.do_normalize:
+            attention_mask = (
+                attention_mask
+                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
+                else None
+            )
             padded_inputs["input_values"] = self.zero_mean_unit_var_norm(
-                padded_inputs["input_values"], input_lengths=input_lengths
+                padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value
             )
 
         if return_tensors is not None:

tests/test_feature_extraction_speech_to_text.py

@@ -136,18 +136,49 @@ def test_call(self):
     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)]
-        inputs = feature_extractor(speech_inputs, padding=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))
+        paddings = ["longest", "max_length", "do_not_pad"]
+        max_lengths = [None, 16, None]
+        var_tolerances = [1e-3, 1e-3, 1e-1]
+        for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
 
-        _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]])
+            inputs = feature_extractor(
+                speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True
+            )
+            input_features = inputs.input_features
+            attention_mask = inputs.attention_mask
+            fbank_feat_lengths = [np.sum(x) for x in attention_mask]
+
+            def _check_zero_mean_unit_variance(input_vector, var_tol=1e-3):
+                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) < var_tol))
+
+            _check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]], var_tol)
+            _check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]], var_tol)
+            _check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]], var_tol)
+
+    def test_cepstral_mean_and_variance_normalization_np(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)]
+
+        paddings = ["longest", "max_length", "do_not_pad"]
+        max_lengths = [None, 16, None]
+        var_tolerances = [1e-3, 1e-3, 1e-1]
+        for max_length, padding, var_tol in zip(max_lengths, paddings, var_tolerances):
+            inputs = feature_extractor(
+                speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True
+            )
+            input_features = inputs.input_features
+            attention_mask = inputs.attention_mask
+            fbank_feat_lengths = [np.sum(x) for x in attention_mask]
+
+            def _check_zero_mean_unit_variance(input_vector, var_tol=1e-3):
+                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) < var_tol))
+
+            _check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]], var_tol)
+            _check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]], var_tol)
+            _check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]], var_tol)
 
     def test_cepstral_mean_and_variance_normalization_trunc(self):
         feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())

tests/test_feature_extraction_wav2vec2.py

@@ -120,21 +120,45 @@ 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))
 
-    def test_zero_mean_unit_variance_normalization(self):
+    def test_zero_mean_unit_variance_normalization_np(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", 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)
+        paddings = ["longest", "max_length", "do_not_pad"]
+        max_lengths = [None, 1600, None]
+        for max_length, padding in zip(max_lengths, paddings):
+            processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
+            input_values = processed.input_values
 
-        _check_zero_mean_unit_variance(input_values[0, :800])
-        _check_zero_mean_unit_variance(input_values[1, :1000])
-        _check_zero_mean_unit_variance(input_values[2])
+            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][:1000])
+            _check_zero_mean_unit_variance(input_values[2][:1200])
+
+    def test_zero_mean_unit_variance_normalization(self):
+        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        lengths = range(800, 1400, 200)
+        speech_inputs = [floats_list((1, x))[0] for x in lengths]
+
+        paddings = ["longest", "max_length", "do_not_pad"]
+        max_lengths = [None, 1600, None]
+
+        for max_length, padding in zip(max_lengths, paddings):
+            processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
+            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][:1000])
+            _check_zero_mean_unit_variance(input_values[2][:1200])
 
-    def test_zero_mean_unit_variance_normalization_trunc(self):
+    def test_zero_mean_unit_variance_normalization_trunc_np(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(