mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
106 lines
3.8 KiB
Python
106 lines
3.8 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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"""
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# Training the model
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Basic run (on CPU for 50 epochs):
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python examples/asr/speech_to_text.py \
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# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
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model.train_ds.manifest_filepath="<path to manifest file>" \
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model.validation_ds.manifest_filepath="<path to manifest file>" \
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trainer.gpus=0 \
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trainer.max_epochs=50
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Add PyTorch Lightning Trainer arguments from CLI:
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python speech_to_text.py \
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... \
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+trainer.fast_dev_run=true
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Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)"
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PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)"
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Override some args of optimizer:
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python speech_to_text.py \
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# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
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model.train_ds.manifest_filepath="./an4/train_manifest.json" \
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model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
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trainer.gpus=2 \
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trainer.max_epochs=2 \
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model.optim.args.betas=[0.8,0.5] \
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model.optim.args.weight_decay=0.0001
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Override optimizer entirely
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python speech_to_text.py \
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# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
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model.train_ds.manifest_filepath="./an4/train_manifest.json" \
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model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
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trainer.gpus=2 \
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trainer.max_epochs=2 \
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model.optim.name=adamw \
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model.optim.lr=0.001 \
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~model.optim.args \
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+model.optim.args.betas=[0.8,0.5]\
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+model.optim.args.weight_decay=0.0005
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# Fine-tune a model
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For documentation on fine-tuning this model, please visit -
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https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
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# Pretrained Models
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For documentation on existing pretrained models, please visit -
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https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html
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"""
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import pytorch_lightning as pl
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from omegaconf import OmegaConf
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from nemo.collections.asr.models import EncDecCTCModel
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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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@hydra_runner(config_path="conf", config_name="config")
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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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exp_manager(trainer, cfg.get("exp_manager", None))
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asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
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# Initialize the weights of the model from another model, if provided via config
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asr_model.maybe_init_from_pretrained_checkpoint(cfg)
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trainer.fit(asr_model)
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if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
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gpu = 1 if cfg.trainer.gpus != 0 else 0
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test_trainer = pl.Trainer(
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gpus=gpu,
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precision=trainer.precision,
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amp_level=trainer.accelerator_connector.amp_level,
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amp_backend=cfg.trainer.get("amp_backend", "native"),
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)
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if asr_model.prepare_test(test_trainer):
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test_trainer.test(asr_model)
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if __name__ == '__main__':
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main() # noqa pylint: disable=no-value-for-parameter
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