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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 pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models.ssl_models import SpeechEncDecSelfSupervisedModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
"""
# Example of unsupervised pre-training of a model
```sh
python speech_pre_training.py \
# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
model.train_ds.manifest_filepath=<path to train manifest> \
model.validation_ds.manifest_filepath=<path to val/test manifest> \
trainer.devices=-1 \
trainer.accelerator="gpu" \
strategy="ddp" \
trainer.max_epochs=100 \
model.optim.name="adamw" \
model.optim.lr=0.001 \
model.optim.betas=[0.9,0.999] \
model.optim.weight_decay=0.0001 \
model.optim.sched.warmup_steps=2000
exp_manager.create_wandb_logger=True \
exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
exp_manager.wandb_logger_kwargs.project="<Namex of project>"
```
For documentation on fine-tuning, please visit -
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
When doing supervised fine-tuning from unsupervised pre-trained encoder, set flag init_strict to False
"""
@hydra_runner(config_path="../conf/ssl/citrinet/", config_name="citrinet_ssl_1024")
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 = SpeechEncDecSelfSupervisedModel(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 __name__ == "__main__":
main()
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