# 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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# which gpu to train or finetune
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export CUDA_VISIBLE_DEVICES="0"
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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# data dir, which contains: train.json, val.json, tokens.jsonl/tokens.txt, am.mvn
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#data_dir="/Users/zhifu/funasr1.0/data/list"
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## generate jsonl from wav.scp and text.txt
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#python -m funasr.datasets.audio_datasets.scp2jsonl \
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#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
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#++data_type_list='["source", "target"]' \
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#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
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train_data="/nfs/maziyang.mzy/data/librispeech/librispeech_train_960h.jsonl"
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val_data="/nfs/maziyang.mzy/data/librispeech/librispeech_dev_other_filtered.jsonl"
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# exp output dir
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output_dir="/nfs/zhifu.gzf/ckpt/exp/llm_asr_whisper_vicuna_exp1"
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log_file="${output_dir}/log.txt"
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workspace=`pwd`
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config="whisper_vicuna_linear.yaml"
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init_param="${output_dir}/model.pt"
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mkdir -p ${output_dir}
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echo "log_file: ${log_file}"
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deepspeed_config=${workspace}/../../ds_stage1.json
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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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torchrun $DISTRIBUTED_ARGS \
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../../../funasr/bin/train_ds.py \
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--config-path "${workspace}/conf" \
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--config-name "${config}" \
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++train_data_set_list="${train_data}" \
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++valid_data_set_list="${val_data}" \
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++dataset_conf.batch_size=4 \
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++dataset_conf.num_workers=4 \
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++train_conf.max_epoch=15 \
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++train_conf.use_deepspeed=false \
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++train_conf.deepspeed_config=${deepspeed_config} \
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++optim_conf.lr=0.0001 \
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++init_param="${init_param}" \
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++output_dir="${output_dir}" &> ${log_file} &
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