Rename spliceout to splice_out

README.md

@@ -213,7 +213,7 @@ classification. It was successfully applied in the paper
 ### Added
 
 * Add new transform: `Identity`
-* Add API for processing of targets alongside inputs. Some transforms experimentally
+* Add API for processing targets alongside inputs. Some transforms experimentally
   support this feature already.
 
 ### Changed

scripts/demo.py

@@ -27,7 +27,7 @@
     Identity,
 )
 from torch_audiomentations.augmentations.shuffle_channels import ShuffleChannels
-from torch_audiomentations.augmentations.spliceout import SpliceOut
+from torch_audiomentations.augmentations.splice_out import SpliceOut
 from torch_audiomentations.core.transforms_interface import ModeNotSupportedException
 from torch_audiomentations.utils.object_dict import ObjectDict
 

tests/test_spliceout.py

@@ -3,12 +3,12 @@
 import torch
 import pytest
 
-from torch_audiomentations.augmentations.spliceout import SpliceOut
+from torch_audiomentations.augmentations.splice_out import SpliceOut
 from torch_audiomentations import Compose
 
 
 class TestSpliceout(unittest.TestCase):
-    def test_spliceout(self):
+    def test_splice_out(self):
 
         audio_samples = torch.rand(size=(8, 1, 32000), dtype=torch.float32)
         augment = Compose(
@@ -17,13 +17,13 @@ def test_spliceout(self):
             ],
             output_type="dict",
         )
-        spliceout_samples = augment(
+        splice_out_samples = augment(
             samples=audio_samples, sample_rate=16000
         ).samples.numpy()
 
-        assert spliceout_samples.dtype == np.float32
+        assert splice_out_samples.dtype == np.float32
 
-    def test_spliceout_odd_hann(self):
+    def test_splice_out_odd_hann(self):
 
         audio_samples = torch.rand(size=(8, 1, 32000), dtype=torch.float32)
         augment = Compose(
@@ -32,13 +32,13 @@ def test_spliceout_odd_hann(self):
             ],
             output_type="dict",
         )
-        spliceout_samples = augment(
+        splice_out_samples = augment(
             samples=audio_samples, sample_rate=16100
         ).samples.numpy()
 
-        assert spliceout_samples.dtype == np.float32
+        assert splice_out_samples.dtype == np.float32
 
-    def test_spliceout_perbatch(self):
+    def test_splice_out_per_batch(self):
 
         audio_samples = torch.rand(size=(8, 1, 32000), dtype=torch.float32)
         augment = Compose(
@@ -53,15 +53,15 @@ def test_spliceout_perbatch(self):
             ],
             output_type="dict",
         )
-        spliceout_samples = augment(
+        splice_out_samples = augment(
             samples=audio_samples, sample_rate=16000
         ).samples.numpy()
 
-        assert spliceout_samples.dtype == np.float32
-        self.assertLess(spliceout_samples.sum(), audio_samples.numpy().sum())
-        self.assertEqual(spliceout_samples.shape, audio_samples.shape)
+        assert splice_out_samples.dtype == np.float32
+        self.assertLess(splice_out_samples.sum(), audio_samples.numpy().sum())
+        self.assertEqual(splice_out_samples.shape, audio_samples.shape)
 
-    def test_spliceout_multichannel(self):
+    def test_splice_out_multichannel(self):
 
         audio_samples = torch.rand(size=(8, 2, 32000), dtype=torch.float32)
         augment = Compose(
@@ -70,17 +70,17 @@ def test_spliceout_multichannel(self):
             ],
             output_type="dict",
         )
-        spliceout_samples = augment(
+        splice_out_samples = augment(
             samples=audio_samples, sample_rate=16000
         ).samples.numpy()
 
-        assert spliceout_samples.dtype == np.float32
-        self.assertLess(spliceout_samples.sum(), audio_samples.numpy().sum())
-        self.assertEqual(spliceout_samples.shape, audio_samples.shape)
+        assert splice_out_samples.dtype == np.float32
+        self.assertLess(splice_out_samples.sum(), audio_samples.numpy().sum())
+        self.assertEqual(splice_out_samples.shape, audio_samples.shape)
 
     @pytest.mark.skip(reason="This test fails and SpliceOut is not released yet")
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA")
-    def test_spliceout_cuda(self):
+    def test_splice_out_cuda(self):
 
         audio_samples = (
             torch.rand(
@@ -94,9 +94,9 @@ def test_spliceout_cuda(self):
             ],
             output_type="dict",
         )
-        spliceout_samples = (
+        splice_out_samples = (
             augment(samples=audio_samples, sample_rate=16000).samples.cpu().numpy()
         )
 
-        assert spliceout_samples.dtype == np.float32
-        self.assertLess(spliceout_samples.sum(), audio_samples.cpu().numpy().sum())
+        assert splice_out_samples.dtype == np.float32
+        self.assertLess(splice_out_samples.sum(), audio_samples.cpu().numpy().sum())