fix: transform applied after resampling, refactor mono/streo transfor…

…m name

README.md

@@ -114,8 +114,8 @@ random_crop = RandomCrop(size=22050*2) # Crop 2 seconds at 22050 Hz from a rando
 from audio_data_pytorch import Resample
 resample = Resample(source=48000, target=22050), # Resamples from 48kHz to 22kHz
 
-from audio_data_pytorch import OverlapChannels
-overlap = OverlapChannels() # Overap channels by sum (C, N) -> (1, N)
+from audio_data_pytorch import Mono
+overlap = Mono() # Overap channels by sum to get mono soruce (C, N) -> (1, N)
 
 from audio_data_pytorch import Stereo
 stereo = Stereo() # Duplicate channels (1, N) -> (2, N) or (2, N) -> (2, N)
@@ -138,7 +138,7 @@ transform = AllTransform(
     random_crop_size: Optional[int] = None,
     loudness: Optional[int] = None,
     scale: Optional[float] = None,
-    overlap_channels: bool = False,
-    use_stereo: bool = False,
+    mono: bool = False,
+    stereo: bool = False,
 )
 ```

audio_data_pytorch/datasets/wav_dataset.py

@@ -36,14 +36,15 @@ def __getitem__(
     ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
         idx = idx.tolist() if torch.is_tensor(idx) else idx  # type: ignore
         waveform, sample_rate = torchaudio.load(self.wavs[idx])
-        if self.transforms:
-            waveform = self.transforms(waveform)
 
         if self.sample_rate and sample_rate != self.sample_rate:
             waveform = torchaudio.transforms.Resample(
                 orig_freq=sample_rate, new_freq=self.sample_rate
             )(waveform)
 
+        if self.transforms:
+            waveform = self.transforms(waveform)
+
         return waveform
 
     def __len__(self) -> int:

audio_data_pytorch/transforms/__init__.py

@@ -1,7 +1,7 @@
 from .all import AllTransform
 from .crop import Crop
 from .loudness import Loudness
-from .overlap_channels import OverlapChannels
+from .mono import Mono
 from .randomcrop import RandomCrop
 from .resample import Resample
 from .scale import Scale

audio_data_pytorch/transforms/all.py

@@ -5,7 +5,7 @@
 from ..utils import exists
 from .crop import Crop
 from .loudness import Loudness
-from .overlap_channels import OverlapChannels
+from .mono import Mono
 from .randomcrop import RandomCrop
 from .resample import Resample
 from .scale import Scale
@@ -21,8 +21,8 @@ def __init__(
         random_crop_size: Optional[int] = None,
         loudness: Optional[int] = None,
         scale: Optional[float] = None,
-        use_stereo: bool = False,
-        overlap_channels: bool = False,
+        stereo: bool = False,
+        mono: bool = False,
     ):
         super().__init__()
 
@@ -38,8 +38,8 @@ def __init__(
             else nn.Identity(),
             RandomCrop(random_crop_size) if exists(random_crop_size) else nn.Identity(),
             Crop(crop_size) if exists(crop_size) else nn.Identity(),
-            OverlapChannels() if overlap_channels else nn.Identity(),
-            Stereo() if use_stereo else nn.Identity(),
+            Mono() if mono else nn.Identity(),
+            Stereo() if stereo else nn.Identity(),
             Loudness(sampling_rate=target_rate, target=loudness)  # type: ignore
             if exists(loudness)
             else nn.Identity(),

audio_data_pytorch/transforms/overlap_channels.py → audio_data_pytorch/transforms/mono.py

@@ -2,7 +2,7 @@
 from torch import Tensor, nn
 
 
-class OverlapChannels(nn.Module):
+class Mono(nn.Module):
     """Overlaps all channels into one"""
 
     def forward(self, x: Tensor) -> Tensor:

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="audio-data-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.0.9",
+    version="0.0.10",
     license="MIT",
     description="Audio Data - PyTorch",
     long_description_content_type="text/markdown",