66 lines
2.0 KiB
Bash
66 lines
2.0 KiB
Bash
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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workspace=`pwd`
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# which gpu to train or finetune
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export CUDA_VISIBLE_DEVICES="0"
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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# model_name from model_hub, or model_dir in local path
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model_name_or_model_dir="FunAudioLLM/Fun-ASR-Nano-2512"
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# data dir, which contains: train.json, val.json
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train_data=${workspace}/data/train_example.jsonl
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val_data=${workspace}/data/val_example.jsonl
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# exp output dir
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output_dir="./outputs"
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log_file="${output_dir}/log.txt"
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deepspeed_config=${workspace}/deepspeed_conf/ds_stage1.json
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mkdir -p ${output_dir}
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echo "log_file: ${log_file}"
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DISTRIBUTED_ARGS="
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--nnodes ${WORLD_SIZE:-1} \
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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# funasr trainer path
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train_tool=`which funasr-train-ds`
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echo "Using funasr trainer: ${train_tool}"
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torchrun $DISTRIBUTED_ARGS \
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${train_tool} \
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++model="${model_name_or_model_dir}" \
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++trust_remote_code=true \
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++train_data_set_list="${train_data}" \
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++valid_data_set_list="${val_data}" \
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++dataset_conf.data_split_num=1 \
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++dataset_conf.batch_sampler="BatchSampler" \
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++dataset_conf.batch_size=6000 \
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++dataset_conf.sort_size=1024 \
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++dataset_conf.batch_type="token" \
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++dataset_conf.num_workers=4 \
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++train_conf.max_epoch=50 \
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++train_conf.log_interval=1 \
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++train_conf.resume=true \
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++train_conf.validate_interval=2000 \
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++train_conf.save_checkpoint_interval=2000 \
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++train_conf.effective_save_name_excludes="None" \
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++train_conf.keep_nbest_models=20 \
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++train_conf.avg_nbest_model=10 \
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++train_conf.use_deepspeed=false \
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++train_conf.deepspeed_config=${deepspeed_config} \
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++optim_conf.lr=0.0002 \
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++audio_encoder_conf.freeze=true \
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++audio_adaptor_conf.freeze=true \
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++llm_conf.freeze=false \
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++output_dir="${output_dir}" &> ${log_file}
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