How to use the lhotse.dataset.source_separation.PreMixedSourceSeparationDataset function in lhotse
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mpariente / AsSteroid / egs / MiniLibriMix / lhotse / train.py View on Github 
def main(conf):
train_set = LhotseDataset(OnTheFlyMixing(), 300, 0)
val_set = LhotseDataset(PreMixedSourceSeparationDataset(sources_set=CutSet.from_yaml('data/cuts_sources.yml.gz'),
mixtures_set=CutSet.from_yaml('data/cuts_mix.yml.gz'),
root_dir="."), 300, 0)
train_loader = DataLoader(train_set, shuffle=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
val_loader = DataLoader(val_set, shuffle=False,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
# Update number of source values (It depends on the task)
#conf['masknet'].update({'n_src': train_set.n_src})
class Model(torch.nn.Module):
def __init__(self, net):lhotse
Data preparation for speech processing models training.
