Replace bg_iterator in examples (#2645)

Summary:
`bg_iterator` was deprecated in 0.11 because it was known to have issues (deadlock) without speed up. Remove instances of `bg_iterator` used in torchaudio examples.

Resolves https://github.com/pytorch/audio/issues/2642

Pull Request resolved: https://github.com/pytorch/audio/pull/2645

Reviewed By: nateanl

Differential Revision: D38954292

Pulled By: carolineechen

fbshipit-source-id: 2333ab5228c2b8511ff532057543aaf9d02b2789

examples/pipeline_wav2letter/main.py

@@ -13,7 +13,6 @@ from languagemodels import LanguageModel
 from torch.optim import Adadelta, Adam, AdamW, SGD
 from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau
 from torch.utils.data import DataLoader
-from torchaudio.datasets.utils import bg_iterator
 from torchaudio.functional import edit_distance
 from torchaudio.models.wav2letter import Wav2Letter
 from transforms import Normalize, UnsqueezeFirst
@@ -246,7 +245,7 @@ def train_one_epoch(
     metric = MetricLogger("train", disable=disable_logger)
     metric["epoch"] = epoch
 
-    for inputs, targets, tensors_lengths, target_lengths in bg_iterator(data_loader, maxsize=2):
+    for inputs, targets, tensors_lengths, target_lengths in data_loader:
 
         start = time()
         inputs = inputs.to(device, non_blocking=True)
@@ -314,7 +313,7 @@ def evaluate(
         metric = MetricLogger("validation", disable=disable_logger)
         metric["epoch"] = epoch
 
-        for inputs, targets, tensors_lengths, target_lengths in bg_iterator(data_loader, maxsize=2):
+        for inputs, targets, tensors_lengths, target_lengths in data_loader:
 
             inputs = inputs.to(device, non_blocking=True)
             targets = targets.to(device, non_blocking=True)

examples/pipeline_wavernn/main.py

@@ -13,7 +13,6 @@ from losses import LongCrossEntropyLoss, MoLLoss
 from processing import NormalizeDB
 from torch.optim import Adam
 from torch.utils.data import DataLoader
-from torchaudio.datasets.utils import bg_iterator
 from torchaudio.models.wavernn import WaveRNN
 from utils import count_parameters, MetricLogger, save_checkpoint
 
@@ -209,7 +208,7 @@ def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch):
     metric = MetricLogger("train_iteration")
     metric["epoch"] = epoch
 
-    for waveform, specgram, target in bg_iterator(data_loader, maxsize=2):
+    for waveform, specgram, target in data_loader:
 
         start2 = time()
 
@@ -258,7 +257,7 @@ def validate(model, criterion, data_loader, device, epoch):
         sums = defaultdict(lambda: 0.0)
         start = time()
 
-        for waveform, specgram, target in bg_iterator(data_loader, maxsize=2):
+        for waveform, specgram, target in data_loader:
 
             waveform = waveform.to(device)
             specgram = specgram.to(device)