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