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# Copyright (c) 2025, 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 os
import torch
from lightning.pytorch import Trainer
from omegaconf import OmegaConf
from nemo.collections.speechlm2 import DataModule, DuplexS2SDataset, DuplexS2SModel
from nemo.core.config import hydra_runner
from nemo.utils.exp_manager import exp_manager
from nemo.utils.trainer_utils import resolve_trainer_cfg
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
@hydra_runner(config_path="conf", config_name="s2s_duplex")
def train(cfg):
OmegaConf.resolve(cfg)
torch.distributed.init_process_group(backend="nccl")
torch.set_float32_matmul_precision("medium")
torch.backends.cudnn.allow_tf32 = True
trainer = Trainer(**resolve_trainer_cfg(cfg.trainer))
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
OmegaConf.save(cfg, log_dir / "exp_config.yaml")
with trainer.init_module():
model = DuplexS2SModel(OmegaConf.to_container(cfg.model, resolve=True))
dataset = DuplexS2SDataset(
tokenizer=model.tokenizer,
frame_length=cfg.data.frame_length,
source_sample_rate=cfg.data.source_sample_rate,
target_sample_rate=cfg.data.target_sample_rate,
input_roles=cfg.data.input_roles,
output_roles=cfg.data.output_roles,
)
datamodule = DataModule(cfg.data, tokenizer=model.tokenizer, dataset=dataset)
trainer.fit(model, datamodule)
if __name__ == "__main__":
train()
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