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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.
"""
# Preparing the Tokenizer for the dataset
Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts/tokenizers/ in order to prepare the tokenizer.
```sh
python <NEMO_ROOT>/scripts/tokenizers/process_asr_text_tokenizer.py \
--manifest=<path to train manifest files, seperated by commas>
OR
--data_file=<path to text data, seperated by commas> \
--data_root="<output directory>" \
--vocab_size=<number of tokens in vocabulary> \
--tokenizer=<"spe" or "wpe"> \
--no_lower_case \
--spe_type=<"unigram", "bpe", "char" or "word"> \
--spe_character_coverage=1.0 \
--log
```
# Training the model
```sh
python speech_to_text_ctc_bpe.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> \
model.tokenizer.dir=<path to directory of tokenizer (not full path to the vocab file!)> \
model.tokenizer.type=<either bpe or wpe> \
trainer.devices=-1 \
trainer.accelerator="gpu" \
trainer.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="<Name of project>"
```
# 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
# Pretrained Models
For documentation on existing pretrained models, please visit -
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html
"""
import pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models.ctc_bpe_models import EncDecCTCModelBPE
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
@hydra_runner(config_path="../conf/citrinet/", config_name="config_bpe")
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 = EncDecCTCModelBPE(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()
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