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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/asr_transducer/speech_to_text_rnnt.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
Add PyTorch Lightning Trainer arguments from CLI:
python speech_to_text_rnnt.py \
... \
+trainer.fast_dev_run=true
Hydra logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/.hydra)"
PTL logs will be found in "$(./outputs/$(date +"%y-%m-%d")/$(date +"%H-%M-%S")/lightning_logs)"
Override some args of optimizer:
python speech_to_text_rnnt.py \
--config-path="experimental/contextnet_rnnt" \
--config-name="config_rnnt" \
model.train_ds.manifest_filepath="./an4/train_manifest.json" \
model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
trainer.devices=2 \
trainer.precision=16 \
trainer.max_epochs=2 \
model.optim.betas=[0.8,0.5] \
model.optim.weight_decay=0.0001
Override optimizer entirely
python speech_to_text_rnnt.py \
--config-path="experimental/contextnet_rnnt" \
--config-name="config_rnnt" \
model.train_ds.manifest_filepath="./an4/train_manifest.json" \
model.validation_ds.manifest_filepath="./an4/test_manifest.json" \
trainer.devices=2 \
trainer.precision=16 \
trainer.max_epochs=2 \
model.optim.name=adamw \
model.optim.lr=0.001 \
~model.optim.args \
+model.optim.args.betas=[0.8,0.5]\
+model.optim.args.weight_decay=0.0005
# Fine-tune a model
For documentation on fine-tuning this model, please visit -
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
"""
import pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models import EncDecRNNTModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="experimental/contextnet_rnnt", config_name="config_rnnt")
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))
asr_model = EncDecRNNTModel(cfg=cfg.model, trainer=trainer)
# Initialize the weights of the model from another model, if provided via config
asr_model.maybe_init_from_pretrained_checkpoint(cfg)
trainer.fit(asr_model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if asr_model.prepare_test(trainer):
trainer.test(asr_model)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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