File size: 3,332 Bytes
a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | # 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 datetime
import os
import torch
from lightning.pytorch import Trainer
from omegaconf import OmegaConf
from nemo.collections.speechlm2 import DataModule, DuplexEARTTSDataset
from nemo.collections.speechlm2.models.duplex_ear_tts import DuplexEARTTS
from nemo.collections.speechlm2.parts.pretrained import load_checkpoint, set_model_dict_for_partial_init
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="duplex_eartts")
def train(cfg):
OmegaConf.resolve(cfg)
torch.distributed.init_process_group(
backend="nccl", timeout=datetime.timedelta(seconds=int(cfg.trainer.strategy.get("timeout", 3600)))
)
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 = DuplexEARTTS(OmegaConf.to_container(cfg, resolve=True))
# load pretrained tts checkpoint if available
if model.cfg.get("pretrained_tts_model", None):
checkpoint_state = load_checkpoint(model.cfg.pretrained_tts_model)
checkpoint_state = set_model_dict_for_partial_init(checkpoint_state, model.tts_model.state_dict())
model.tts_model.load_state_dict(checkpoint_state, strict=True)
# load pretrained checkpoint and rescale the weights if needed
if model.cfg.get("pretrained_model", None):
model.restore_from_pretrained_checkpoint(model.cfg.pretrained_model)
dataset = DuplexEARTTSDataset(
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,
add_text_bos_and_eos_in_each_turn=cfg.data.get("add_text_bos_and_eos_in_each_turn", True),
add_audio_prompt=cfg.data.get("add_audio_prompt", True),
audio_prompt_duration=cfg.data.get("audio_prompt_duration", 3),
num_delay_speech_tokens=cfg.model.get("num_delay_speech_tokens", 2),
add_system_prompt=cfg.model.get("use_system_prompt", False),
ignore_data_system_prompt=cfg.model.get("ignore_data_system_prompt", False),
)
datamodule = DataModule(cfg.data, tokenizer=model.tokenizer, dataset=dataset)
trainer.fit(model, datamodule)
if __name__ == "__main__":
train()
|