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58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
# Copyright (c) 2020, 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 os
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import lightning.pytorch as pl
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import torch
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from lightning.pytorch import seed_everything
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from omegaconf import OmegaConf
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from nemo.collections.asr.models import EncDecSpeakerLabelModel
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from nemo.core.config import hydra_runner
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from nemo.utils import logging
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from nemo.utils.exp_manager import exp_manager
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seed_everything(42)
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@hydra_runner(config_path="conf", config_name="titanet-finetune.yaml")
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def main(cfg):
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logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
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trainer = pl.Trainer(**cfg.trainer)
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log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
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speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
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speaker_model.maybe_init_from_pretrained_checkpoint(cfg)
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# save labels to file
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if log_dir is not None:
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with open(os.path.join(log_dir, 'labels.txt'), 'w') as f:
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if speaker_model.labels is not None:
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for label in speaker_model.labels:
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f.write(f'{label}\n')
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trainer.fit(speaker_model)
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torch.distributed.destroy_process_group()
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if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
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if trainer.is_global_zero:
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trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator, strategy=cfg.trainer.strategy)
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if speaker_model.prepare_test(trainer):
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trainer.test(speaker_model)
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if __name__ == '__main__':
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main()
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