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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.
import os
import pytorch_lightning as pl
import torch
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything
from nemo.collections.asr.models import EncDecSpeakerLabelModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
seed_everything(42)
@hydra_runner(config_path="conf", config_name="titanet-finetune.yaml")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
speaker_model.maybe_init_from_pretrained_checkpoint(cfg)
# save labels to file
if log_dir is not None:
with open(os.path.join(log_dir, 'labels.txt'), 'w') as f:
if speaker_model.labels is not None:
for label in speaker_model.labels:
f.write(f'{label}\n')
trainer.fit(speaker_model)
torch.distributed.destroy_process_group()
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if trainer.is_global_zero:
trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator, strategy=cfg.trainer.strategy)
if speaker_model.prepare_test(trainer):
trainer.test(speaker_model)
if __name__ == '__main__':
main()
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