# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

workspace=`pwd`

# which gpu to train or finetune
export CUDA_VISIBLE_DEVICES="0"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')

# model_name from model_hub, or model_dir in local path
model_name_or_model_dir="FunAudioLLM/Fun-ASR-Nano-2512"

# data dir, which contains: train.json, val.json
train_data=${workspace}/data/train_example.jsonl
val_data=${workspace}/data/val_example.jsonl

# exp output dir
output_dir="./outputs"
log_file="${output_dir}/log.txt"

deepspeed_config=${workspace}/deepspeed_conf/ds_stage1.json

mkdir -p ${output_dir}
echo "log_file: ${log_file}"

DISTRIBUTED_ARGS="
    --nnodes ${WORLD_SIZE:-1} \
    --nproc_per_node $gpu_num \
    --node_rank ${RANK:-0} \
    --master_addr ${MASTER_ADDR:-127.0.0.1} \
    --master_port ${MASTER_PORT:-26669}
"
echo $DISTRIBUTED_ARGS

# funasr trainer path
train_tool=`which funasr-train-ds`
echo "Using funasr trainer: ${train_tool}"

torchrun $DISTRIBUTED_ARGS \
${train_tool} \
++model="${model_name_or_model_dir}" \
++trust_remote_code=true \
++train_data_set_list="${train_data}" \
++valid_data_set_list="${val_data}" \
++dataset_conf.data_split_num=1 \
++dataset_conf.batch_sampler="BatchSampler" \
++dataset_conf.batch_size=6000  \
++dataset_conf.sort_size=1024 \
++dataset_conf.batch_type="token" \
++dataset_conf.num_workers=4 \
++train_conf.max_epoch=50 \
++train_conf.log_interval=1 \
++train_conf.resume=true \
++train_conf.validate_interval=2000 \
++train_conf.save_checkpoint_interval=2000 \
++train_conf.keep_nbest_models=20 \
++train_conf.avg_nbest_model=10 \
++train_conf.use_deepspeed=false \
++train_conf.deepspeed_config=${deepspeed_config} \
++optim_conf.lr=0.0002 \
++audio_encoder_conf.freeze=true \
++audio_adaptor_conf.freeze=true \
++llm_conf.freeze=false \
++output_dir="${output_dir}" &> ${log_file}