add padding

enhancer/data/dataset.py

@@ -59,7 +59,7 @@ class TaskDataset(pl.LightningDataModule):
         name: str,
         root_dir: str,
         files: Files,
-        valid_minutes: float = 0.20,
+        min_valid_minutes: float = 0.20,
         duration: float = 1.0,
         stride=None,
         sampling_rate: int = 48000,
@@ -81,10 +81,10 @@ class TaskDataset(pl.LightningDataModule):
         if num_workers is None:
             num_workers = multiprocessing.cpu_count() // 2
         self.num_workers = num_workers
-        if valid_minutes > 0.0:
-            self.valid_minutes = valid_minutes
+        if min_valid_minutes > 0.0:
+            self.min_valid_minutes = min_valid_minutes
         else:
-            raise ValueError("valid_minutes must be greater than 0")
+            raise ValueError("min_valid_minutes must be greater than 0")
 
         self.augmentations = augmentations
 
@@ -102,7 +102,9 @@ class TaskDataset(pl.LightningDataModule):
             )
             train_data = fp.prepare_matching_dict()
             train_data, self.val_data = self.train_valid_split(
-                train_data, valid_minutes=self.valid_minutes, random_state=42
+                train_data,
+                min_valid_minutes=self.min_valid_minutes,
+                random_state=42,
             )
 
             self.train_data = self.prepare_traindata(train_data)
@@ -117,10 +119,10 @@ class TaskDataset(pl.LightningDataModule):
             self._test = self.prepare_mapstype(test_data)
 
     def train_valid_split(
-        self, data, valid_minutes: float = 20, random_state: int = 42
+        self, data, min_valid_minutes: float = 20, random_state: int = 42
     ):
 
-        valid_minutes *= 60
+        min_valid_minutes *= 60
         valid_sec_now = 0.0
         valid_indices = []
         all_speakers = np.unique(
@@ -129,7 +131,7 @@ class TaskDataset(pl.LightningDataModule):
         possible_indices = list(range(0, len(all_speakers)))
         rng = create_unique_rng(len(all_speakers))
 
-        while valid_sec_now <= valid_minutes:
+        while valid_sec_now <= min_valid_minutes:
             speaker_index = rng.choice(possible_indices)
             possible_indices.remove(speaker_index)
             speaker_name = all_speakers[speaker_index]
@@ -257,10 +259,15 @@ class EnhancerDataset(TaskDataset):
     files : Files
         dataclass containing train_clean, train_noisy, test_clean, test_noisy
         folder names (refer enhancer.utils.Files dataclass)
+    min_valid_minutes: float
+        minimum validation split size time in minutes
+        algorithm randomly select n speakers (>=min_valid_minutes) from train data to form validation data.
     duration : float
         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
@@ -271,6 +278,7 @@ class EnhancerDataset(TaskDataset):
         use one_to_many mapping for multiple noisy files for each clean file
 
 
+
     """
 
     def __init__(
@@ -278,10 +286,11 @@ class EnhancerDataset(TaskDataset):
         name: str,
         root_dir: str,
         files: Files,
-        valid_minutes=5.0,
+        min_valid_minutes=5.0,
         duration=1.0,
         stride=None,
         sampling_rate=48000,
+        padding_mode: str = "silent",
         matching_function=None,
         batch_size=32,
         num_workers: Optional[int] = None,
@@ -292,7 +301,7 @@ class EnhancerDataset(TaskDataset):
             name=name,
             root_dir=root_dir,
             files=files,
-            valid_minutes=valid_minutes,
+            min_valid_minutes=min_valid_minutes,
             sampling_rate=sampling_rate,
             duration=duration,
             matching_function=matching_function,
@@ -306,6 +315,7 @@ 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):
 
@@ -344,6 +354,7 @@ class EnhancerDataset(TaskDataset):
                     self.duration * self.sampling_rate - clean_segment.shape[-1]
                 ),
             ),
+            mode=self.padding_mode,
         )
         noisy_segment = F.pad(
             noisy_segment,
@@ -353,6 +364,7 @@ class EnhancerDataset(TaskDataset):
                     self.duration * self.sampling_rate - noisy_segment.shape[-1]
                 ),
             ),
+            mode=self.padding_mode,
         )
         return {
             "clean": clean_segment,