rmv padding_mode

enhancer/data/dataset.py

@@ -8,7 +8,7 @@ import numpy as np
 import pytorch_lightning as pl
 import torch
 import torch.nn.functional as F
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import DataLoader, Dataset, RandomSampler
 from torch_audiomentations import Compose
 
 from enhancer.data.fileprocessor import Fileprocessor
@@ -135,6 +135,7 @@ class TaskDataset(pl.LightningDataModule):
             speaker_index = rng.choice(possible_indices)
             possible_indices.remove(speaker_index)
             speaker_name = all_speakers[speaker_index]
+            print(f"Selected f{speaker_name} for valid")
             file_indices = [
                 i
                 for i, file in enumerate(data)
@@ -222,11 +223,13 @@ class TaskDataset(pl.LightningDataModule):
         return generator.manual_seed(seed)
 
     def train_dataloader(self):
+        dataset = TrainDataset(self)
+        sampler = RandomSampler(dataset, generator=self.generator)
         return DataLoader(
-            TrainDataset(self),
+            dataset,
             batch_size=self.batch_size,
             num_workers=self.num_workers,
-            generator=self.generator,
+            sampler=sampler,
             collate_fn=self.train_collatefn,
         )
 
@@ -263,8 +266,6 @@ class EnhancerDataset(TaskDataset):
         expected audio duration of single audio sample for training
     sampling_rate : int
         desired sampling rate
-    padding_mode: str
-        padding mode (silent,reflect)
     batch_size : int
         batch size of each batch
     num_workers : int
@@ -287,7 +288,6 @@ class EnhancerDataset(TaskDataset):
         duration=1.0,
         stride=None,
         sampling_rate=48000,
-        padding_mode: str = "silent",
         matching_function=None,
         batch_size=32,
         num_workers: Optional[int] = None,
@@ -312,7 +312,6 @@ class EnhancerDataset(TaskDataset):
         self.duration = max(1.0, duration)
         self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True)
         self.stride = stride or duration
-        self.padding_mode = padding_mode
 
     def setup(self, stage: Optional[str] = None):
 
@@ -337,7 +336,6 @@ class EnhancerDataset(TaskDataset):
         return self.prepare_segment(*self._test[idx])
 
     def prepare_segment(self, file_dict: dict, start_time: float):
-
         clean_segment = self.audio(
             file_dict["clean"], offset=start_time, duration=self.duration
         )
@@ -362,7 +360,6 @@ class EnhancerDataset(TaskDataset):
                     self.duration * self.sampling_rate - noisy_segment.shape[-1]
                 ),
             ),
-            mode=self.padding_mode,
         )
         return {
             "clean": clean_segment,