def setup_dataloader(self):
args = self.args
config = self.config
ljspeech_dataset = LJSpeech(args.data)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
batch_fn = LJSpeechCollector(padding_idx=config.data.padding_idx)
if not self.parallel:
self.train_loader = DataLoader(train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True,
collate_fn=batch_fn)
else:
sampler = DistributedBatchSampler(
train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True)
self.train_loader = DataLoader(train_set,
batch_sampler=sampler,
collate_fn=batch_fn)
self.valid_loader = DataLoader(valid_set,
batch_size=config.data.batch_size,
shuffle=False,
drop_last=False,
collate_fn=batch_fn)
def setup_dataloader(self):
config = self.config
args = self.args
ljspeech_dataset = LJSpeech(args.data)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
# convolutional net's causal padding size
context_size = config.model.n_stack \
* sum([(config.model.filter_size - 1) * 2**i for i in range(config.model.n_loop)]) \
+ 1
context_frames = context_size // config.data.hop_length
# frames used to compute loss
frames_per_second = config.data.sample_rate // config.data.hop_length
train_clip_frames = math.ceil(config.data.train_clip_seconds *
frames_per_second)
num_frames = train_clip_frames + context_frames
batch_fn = LJSpeechClipCollector(num_frames, config.data.hop_length)
if not self.parallel:
train_loader = DataLoader(train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True,
collate_fn=batch_fn)
else:
sampler = DistributedBatchSampler(
train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True)
train_loader = DataLoader(train_set,
batch_sampler=sampler,
collate_fn=batch_fn)
valid_batch_fn = LJSpeechCollector()
valid_loader = DataLoader(valid_set,
batch_size=1,
collate_fn=valid_batch_fn)
self.train_loader = train_loader
self.valid_loader = valid_loader
def setup_dataloader(self):
args = self.args
config = self.config
ljspeech_dataset = LJSpeech(args.data)
transform = Transform(config.data.mel_start_value,
config.data.mel_end_value)
ljspeech_dataset = dataset.TransformDataset(ljspeech_dataset,
transform)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
batch_fn = LJSpeechCollector(padding_idx=config.data.padding_idx)
if not self.parallel:
train_loader = DataLoader(train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True,
collate_fn=batch_fn)
else:
sampler = DistributedBatchSampler(
train_set,
batch_size=config.data.batch_size,
num_replicas=dist.get_world_size(),
rank=dist.get_rank(),
shuffle=True,
drop_last=True)
train_loader = DataLoader(train_set,
batch_sampler=sampler,
collate_fn=batch_fn)
valid_loader = DataLoader(valid_set,
batch_size=config.data.batch_size,
collate_fn=batch_fn)
self.train_loader = train_loader
self.valid_loader = valid_loader
def setup_dataloader(self):
config = self.config
args = self.args
ljspeech_dataset = LJSpeech(args.data)
valid_set, train_set = dataset.split(ljspeech_dataset,
config.data.valid_size)
batch_fn = LJSpeechClipCollector(config.data.clip_frames,
config.data.hop_length)
if not self.parallel:
train_loader = DataLoader(train_set,
batch_size=config.data.batch_size,
shuffle=True,
drop_last=True,
collate_fn=batch_fn)
else:
sampler = DistributedBatchSampler(
train_set,
batch_size=config.data.batch_size,
num_replicas=dist.get_world_size(),
rank=dist.get_rank(),
shuffle=True,
drop_last=True)
train_loader = DataLoader(train_set,
batch_sampler=sampler,
collate_fn=batch_fn)
valid_batch_fn = LJSpeechCollector()
valid_loader = DataLoader(valid_set,
batch_size=1,
collate_fn=valid_batch_fn)
self.train_loader = train_loader
self.valid_loader = valid_loader