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# Copyright (c) 2024, 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
```sh
python speech_to_text_aed.py \
# (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
model.train_ds.tarred_audio_filepaths=<path to tar files with audio> \
model.train_ds.manifest_filepath=<path to audio data manifest> \
model.train_ds.batch_duration=360 \
model.train_ds.num_buckets=30 \
model.train_ds.bucket_duration_bins=<optional list of precomputed float bins for bucket durations, speeds up init> \
model.validation_ds.manifest_filepath=<path to validation manifest> \
model.test_ds.manifest_filepath=<path to test manifest> \
model.model_defaults.asr_enc_hidden=1024 \
model.model_defaults.lm_enc_hidden=512 \
model.model_defaults.lm_dec_hidden=1024 \
model.tokenizer.langs.spl_tokens.dir=<path to the directory of prompt special tokens tokenizer> \
model.tokenizer.langs.spl_tokens.type=bpe \
model.tokenizer.langs.en.dir=<path to the directory of en language tokenizer (add new langs the same way)> \
model.tokenizer.langs.en.type=bpe \
model.prompt_format="canary" \
trainer.devices=-1 \
trainer.accelerator="ddp" \
trainer.max_steps=100000 \
+trainer.limit_train_batches=20000 \
trainer.val_check_interval=5000 \
+trainer.use_distributed_sampler=false \
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>"
```
"""
import torch.multiprocessing as mp
import lightning.pytorch as pl
from omegaconf import OmegaConf
import torch
from nemo.collections.asr.models import EncDecMultiTaskModel
from nemo.core.config import hydra_runner
from nemo.utils import logging, model_utils
from nemo.utils.exp_manager import exp_manager
from nemo.utils.trainer_utils import resolve_trainer_cfg
@hydra_runner(config_path="../conf/speech_multitask/", config_name="fast-conformer_aed")
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer))
exp_manager(trainer, cfg.get("exp_manager", None))
# Check for spl tokens to create spl_tokenizer.
if cfg.get("spl_tokens"):
logging.info("Detected spl_tokens config. Building tokenizer.")
spl_cfg = cfg["spl_tokens"]
spl_tokenizer_cls = model_utils.import_class_by_path(cfg.model.tokenizer.custom_tokenizer["_target_"])
spl_tokenizer_cls.build_special_tokenizer(
spl_cfg["tokens"], spl_cfg["model_dir"], force_rebuild=spl_cfg["force_rebuild"]
)
cfg.model.tokenizer.langs.spl_tokens.dir = spl_cfg["model_dir"]
aed_model = EncDecMultiTaskModel(cfg=cfg.model, trainer=trainer)
# Initialize the weights of the model from another model, if provided via config
aed_model.maybe_init_from_pretrained_checkpoint(cfg)
trainer.fit(aed_model)
if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
if aed_model.prepare_test(trainer):
trainer.test(aed_model)
if __name__ == '__main__':
torch.set_float32_matmul_precision('high')
mp.set_start_method('spawn', force=True)
main()
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