# 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. """ This is an example script for training (SFT/PEFT) multi-modal speech-to-text LLM using NeMo. All SpeechLMs that has the three componnets (audio encoder, modality adapter and LLM) are supported. Some example models are: - SALM (https://arxiv.org/abs/2310.09424) - VoiceTextBlender (https://arxiv.org/abs/2410.17485) Example usage: export WANDB_API_KEY=${WANDB} && \ export CUDA_VISIBLE_DEVICES="1" && \ export HF_TOKEN=${HFTOKEN} && \ export HF_HOME="/home/heh/.huggingface/" && \ export HF_HUB_CACHE="/media/data/cache" && \ export NEMO_MODELS_CACHE="/media/data/pretrained_models/" && \ python speech_to_text_llm_train.py \ --config-path="/home/heh/github/NeMo-main/examples/speechlm/conf/salm" \ --config-name "salm_llama3.2-1b_fc_fc_peft" \ data.train_ds.manifest_filepath=$TRAIN_MANIFESTS \ data.validation_ds.manifest_filepath=$VAL_MANIFESTS \ data.train_ds.num_workers=$NUM_WORKERS \ data.validation_ds.num_workers=$NUM_WORKERS \ ++data.validation_ds.name=$VAL_NAMES \ data.common.global_batch_size=$GLOBAL_BATCH \ data.common.micro_batch_size=$MICRO_BATCH \ strategy.tensor_model_parallel_size=$TP \ trainer.max_steps=1000000 \ trainer.val_check_interval=20 \ strategy.ckpt_async_save=false \ # This is important for `max_time_per_run` to work max_time_per_run="00:03:50:00" # 3 hours 50 minutes, set to 'null' to disable """ from nemo.collections.speechlm.recipes import speech_to_text_llm_train from nemo.core.config import hydra_runner @hydra_runner(config_path="./conf/salm", config_name="salm_llama3.2-1b_fc_fc_peft") def main(cfg): """main function for training.""" return speech_to_text_llm_train(cfg) if __name__ == "__main__": main()