mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-23 08:26:06 +00:00
Brandon Thomas
45 lines
1.8 KiB
Python
Executable File
45 lines
1.8 KiB
Python
Executable File
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytorch_lightning as pl
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from nemo.collections.common.callbacks import LogEpochTimeCallback
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from nemo.collections.tts.models import Tacotron2Model
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from nemo.core.config import hydra_runner
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from nemo.utils.exp_manager import exp_manager
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# hydra_runner is a thin NeMo wrapper around Hydra
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# It looks for a config named tacotron2.yaml inside the conf folder
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# Hydra parses the yaml and returns it as a Omegaconf DictConfig
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@hydra_runner(config_path="conf", config_name="tacotron2")
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def main(cfg):
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# Define the Lightning trainer
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trainer = pl.Trainer(**cfg.trainer)
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# exp_manager is a NeMo construct that helps with logging and checkpointing
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exp_manager(trainer, cfg.get("exp_manager", None))
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# Define the Tacotron 2 model, this will construct the model as well as
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# define the training and validation dataloaders
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model = Tacotron2Model(cfg=cfg.model, trainer=trainer)
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# Let's add a few more callbacks
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lr_logger = pl.callbacks.LearningRateMonitor()
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epoch_time_logger = LogEpochTimeCallback()
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trainer.callbacks.extend([lr_logger, epoch_time_logger])
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# Call lightning trainer's fit() to train the model
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trainer.fit(model)
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if __name__ == '__main__':
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main() # noqa pylint: disable=no-value-for-parameter
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