| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import pytorch_lightning as pl | |
| from nemo.collections.common.callbacks import LogEpochTimeCallback | |
| from nemo.collections.tts.models import Tacotron2Model | |
| from nemo.core.config import hydra_runner | |
| from nemo.utils.exp_manager import exp_manager | |
| # hydra_runner is a thin NeMo wrapper around Hydra | |
| # It looks for a config named tacotron2.yaml inside the conf folder | |
| # Hydra parses the yaml and returns it as a Omegaconf DictConfig | |
| 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) | |
| if __name__ == '__main__': | |
| main() # noqa pylint: disable=no-value-for-parameter | |