# 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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# method1, finetune from model hub
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# which gpu to train or finetune
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export CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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# data dir, which contains: train.json, val.json
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data_dir="/Users/zhifu/funasr1.0/data/list"
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## generate jsonl from wav.scp and text.txt
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#python -m funasr.datasets.audio_datasets.scp2jsonl \
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#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
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#++data_type_list='["source", "target"]' \
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#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
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train_data="${data_dir}/train.jsonl"
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val_data="${data_dir}/val.jsonl"
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# exp output dir
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output_dir="/Users/zhifu/exp"
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log_file="${output_dir}/log.txt"
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mkdir -p ${output_dir}
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echo "log_file: ${log_file}"
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torchrun \
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--nnodes 1 \
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--nproc_per_node ${gpu_num} \
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funasr/bin/train.py \
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++model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
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++model_revision="v2.0.4" \
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++train_data_set_list="${train_data}" \
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++valid_data_set_list="${val_data}" \
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++dataset_conf.batch_size=32 \
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++dataset_conf.batch_type="example" \
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++dataset_conf.num_workers=4 \
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++train_conf.max_epoch=20 \
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++optim_conf.lr=0.0002 \
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++output_dir="${output_dir}" &> ${log_file}
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