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# Copyright (c) 2022, 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 omegaconf import OmegaConf
from pytorch_lightning import seed_everything
from nemo.collections.asr.models import EncDecDiarLabelModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
"""
Example training session (single GPU training on telephonic datasets)
python ./multiscale_diar_decoder.py --config-path='../conf/neural_diarizer' --config-name='msdd_5scl_15_05_50Povl_256x3x32x2.yaml' \
trainer.devices=1 \
model.base.diarizer.speaker_embeddings.model_path="titanet_large" \
model.train_ds.manifest_filepath="<train_manifest_path>" \
model.validation_ds.manifest_filepath="<dev_manifest_path>" \
model.train_ds.emb_dir="<train_temp_dir>" \
model.validation_ds.emb_dir="<dev_temp_dir>" \
exp_manager.name='sample_train' \
exp_manager.exp_dir='./msdd_exp'
"""
seed_everything(42)
@hydra_runner(config_path="../conf/neural_diarizer", config_name="msdd_5scl_15_05_50Povl_256x3x32x2.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
msdd_model = EncDecDiarLabelModel(cfg=cfg.model, trainer=trainer)
trainer.fit(msdd_model)
if __name__ == '__main__':
main()
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