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# Copyright (c) 2020, 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.
"""
# Training the model
Basic run (on CPU for 50 epochs):
python examples/asr/experimental/audio_to_audio/speech_enhancement.py \
# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
model.train_ds.manifest_filepath="<path to manifest file>" \
model.validation_ds.manifest_filepath="<path to manifest file>" \
trainer.devices=1 \
trainer.accelerator='cpu' \
trainer.max_epochs=50
PyTorch Lightning Trainer arguments and args of the model and the optimizer can be added or overriden from CLI
"""
import pytorch_lightning as pl
import torch
from omegaconf import OmegaConf
from nemo.collections.asr.models import EncMaskDecAudioToAudioModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="./conf", config_name="multichannel_enhancement")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg, resolve=True)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = EncMaskDecAudioToAudioModel(cfg=cfg.model, trainer=trainer)
# Initialize the weights of the model from another model, if provided via config
model.maybe_init_from_pretrained_checkpoint(cfg)
# Train the model
trainer.fit(model)
# Run on test data, if available
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if trainer.is_global_zero:
# Destroy the current process group and let the trainer initialize it again with a single device.
if torch.distributed.is_initialized():
torch.distributed.destroy_process_group()
# Run test on a single device
trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator)
if model.prepare_test(trainer):
trainer.test(model)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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