76 lines
2.3 KiB
Bash
76 lines
2.3 KiB
Bash
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# 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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# method2, finetune from local model
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workspace=`pwd`
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echo "current path: ${workspace}" # /xxxx/funasr/examples/industrial_data_pretraining/paraformer
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# download model
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local_path_root=${workspace}/modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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# which gpu to train or finetune
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export CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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# data dir, which contains: train.json, val.json
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data_dir="../../../data/list"
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train_data="${data_dir}/train.jsonl"
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val_data="${data_dir}/val.jsonl"
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# generate train.jsonl and val.jsonl from wav.scp and text.txt
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python -m funasr.datasets.audio_datasets.scp2jsonl \
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++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out="${train_data}"
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python -m funasr.datasets.audio_datasets.scp2jsonl \
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++scp_file_list='["../../../data/list/val_wav.scp", "../../../data/list/val_text.txt"]' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out="${val_data}"
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tokens="${local_path}/tokens.json"
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cmvn_file="${local_path}/am.mvn"
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# output dir
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output_dir="./outputs"
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log_file="${output_dir}/log.txt"
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config_name="config.yaml"
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init_param="${local_path}/model.pt"
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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 ${gpu_num} \
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../../../funasr/bin/train.py \
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--config-path "${local_path}" \
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--config-name "${config_name}" \
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++train_data_set_list="${train_data}" \
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++valid_data_set_list="${val_data}" \
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++dataset_conf.batch_size=20000 \
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++dataset_conf.batch_type="token" \
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++dataset_conf.num_workers=4 \
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++train_conf.max_epoch=50 \
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++train_conf.log_interval=10 \
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++train_conf.resume=false \
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++train_conf.validate_interval=15 \
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++train_conf.save_checkpoint_interval=15 \
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++train_conf.keep_nbest_models=50 \
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++optim_conf.lr=0.0002 \
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++init_param="${init_param}" \
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++tokenizer_conf.token_list="${tokens}" \
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++frontend_conf.cmvn_file="${cmvn_file}" \
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++output_dir="${output_dir}" &> ${log_file}
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