def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = HifiGanModel(cfg=cfg.model, trainer=trainer)
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(plugins=[DDPPlugin(find_unused_parameters=True)], **cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = MelGanModel(cfg=cfg.model, trainer=trainer)
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get('exp_manager', None))
model = TalkNetDursModel(cfg=cfg.model, trainer=trainer)
lr_logger = pl.callbacks.LearningRateLogger()
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([lr_logger, epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = FastPitchModel(cfg=cfg.model, trainer=trainer)
lr_logger = pl.callbacks.LearningRateMonitor()
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([lr_logger, epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get('exp_manager', None))
model = TalkNetPitchModel(cfg=cfg.model)
trainer.callbacks.extend(
[pl.callbacks.LearningRateMonitor(),
LogEpochTimeCallback()]) # noqa
trainer.fit(model)
def main(cfg):
preprocess_linear_specs_dataset(**cfg.model.preprocessor)
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = EDMel2SpecModel(cfg=cfg.model, trainer=trainer)
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(plugins=[DDPPlugin(find_unused_parameters=True)],
**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = FastPitchHifiGanE2EModel(cfg=cfg.model, trainer=trainer)
lr_logger = pl.callbacks.LearningRateMonitor()
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([lr_logger, epoch_time_logger])
trainer.fit(model)
def main(cfg):
# Define the Lightning trainer
trainer = pl.Trainer(**cfg.trainer)
# exp_manager is a NeMo construct that helps with logging and checkpointing
exp_manager(trainer, cfg.get("exp_manager", None))
# Define the Tacotron 2 model, this will construct the model as well as
# define the training and validation dataloaders
model = Tacotron2Model(cfg=cfg.model, trainer=trainer)
# Let's add a few more callbacks
lr_logger = pl.callbacks.LearningRateMonitor()
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([lr_logger, epoch_time_logger])
# Call lightning trainer's fit() to train the model
trainer.fit(model)
def main(cfg):
if hasattr(cfg.model.optim, 'sched'):
logging.warning(
"You are using an optimizer scheduler while finetuning. Are you sure this is intended?"
)
if cfg.model.optim.lr > 1e-3 or cfg.model.optim.lr < 1e-5:
logging.warning("The recommended learning rate for finetuning is 2e-4")
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = FastPitchModel(cfg=cfg.model, trainer=trainer)
model.maybe_init_from_pretrained_checkpoint(cfg=cfg)
lr_logger = pl.callbacks.LearningRateMonitor()
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([lr_logger, epoch_time_logger])
trainer.fit(model)
def main(cfg):
trainer = pl.Trainer(**cfg.trainer)
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([epoch_time_logger])
exp_manager(trainer, cfg.get("exp_manager", None))
if cfg.resume_from_ckpt is None:
logging.info("Training UniGlow from scratch")
model = UniGlowModel(cfg=cfg.model, trainer=trainer)
else:
logging.info("Fine-tuning UniGlow from {cfg.resume_from_ckpt}")
model = UniGlowModel.restore_from(cfg.resume_from_ckpt)
model.setup_training_data(cfg.model.train_ds)
model.setup_validation_data(cfg.model.validation_ds)
trainer.fit(model)
