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 fastpitch_model():
test_root = os.path.dirname(os.path.abspath(__file__))
conf = OmegaConf.load(os.path.join(test_root, '../../../examples/tts/conf/fastpitch.yaml'))
conf.train_dataset = conf.validation_datasets = '.'
conf.model.train_ds = conf.model.test_ds = conf.model.validation_ds = None
model = FastPitchModel(cfg=conf.model)
return 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 fastpitch_model():
model = FastPitchModel.from_pretrained(model_name="tts_en_fastpitch")
return model