Update modeling_wav2vec2.py (#15423)

* Update modeling_wav2vec2.py

With very tiny sound files (less than 0.1 seconds) the num_masked_span can be too long. The issue is described in issue #15366 and discussed with @patrickvonplaten.

* correct errors with mask time indices

* remove bogus file

* make fix-copies

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/transformers/models/hubert/modeling_hubert.py

@@ -114,10 +114,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -148,7 +152,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -170,6 +180,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/sew/modeling_sew.py

@@ -115,10 +115,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -149,7 +153,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -171,6 +181,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/sew_d/modeling_sew_d.py

@@ -117,10 +117,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -151,7 +155,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -173,6 +183,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/unispeech/modeling_unispeech.py

@@ -179,10 +179,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -213,7 +217,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -235,6 +245,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -218,10 +218,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -252,7 +256,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -274,6 +284,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -233,10 +233,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -267,7 +271,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -289,6 +299,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

src/transformers/models/wavlm/modeling_wavlm.py

@@ -184,10 +184,14 @@ def _compute_mask_indices(
         num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
         num_masked_span = max(num_masked_span, min_masks)
 
-        # make sure num masked indices <= sequence_length
+        # make sure num masked span <= sequence_length
         if num_masked_span * mask_length > sequence_length:
             num_masked_span = sequence_length // mask_length
 
+        # make sure num_masked span is also <= input_length - (mask_length - 1)
+        if input_length - (mask_length - 1) < num_masked_span:
+            num_masked_span = max(input_length - (mask_length - 1), 0)
+
         return num_masked_span
 
     # compute number of masked spans in batch
@@ -218,7 +222,13 @@ def _compute_mask_indices(
         # pick first sampled index that will serve as a dummy index to pad vector
         # to ensure same dimension for all batches due to probabilistic rounding
         # Picking first sample just pads those vectors twice.
-        dummy_mask_idx = spec_aug_mask_idx[0]
+        if len(spec_aug_mask_idx) == 0:
+            # this case can only happen if `input_length` is strictly smaller then
+            # `sequence_length` in which case the last token has to be a padding
+            # token which we can use as a dummy mask id
+            dummy_mask_idx = sequence_length - 1
+        else:
+            dummy_mask_idx = spec_aug_mask_idx[0]
 
         spec_aug_mask_idx = np.concatenate(
             [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
@@ -240,6 +250,10 @@ def _compute_mask_indices(
     )
     spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
 
+    # ensure that we cannot have indices larger than sequence_length
+    if spec_aug_mask_idxs.max() > sequence_length - 1:
+        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
     # scatter indices to mask
     np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
 

tests/test_modeling_wav2vec2.py

@@ -990,6 +990,23 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
 
         self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
 
+    def test_compute_mask_indices_short_audio(self):
+        batch_size = 4
+        sequence_length = 100
+        mask_prob = 0.05
+        mask_length = 10
+
+        attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
+        # force one example to be heavily padded
+        attention_mask[0, 5:] = 0
+
+        mask = _compute_mask_indices(
+            (batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
+        )
+
+        # make sure that non-padded examples cannot be padded
+        self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
+
     def test_compute_perplexity(self):
         probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100