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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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| | | cmd="funasr/bin/train.py" |
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| | | # method1, finetune from model hub |
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| | | python $cmd \ |
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| | | +model="/Users/zhifu/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ |
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| | | +token_list="/Users/zhifu/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \ |
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| | | +train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \ |
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| | | +output_dir="/Users/zhifu/Downloads/ckpt/funasr2/exp2" \ |
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| | | +device="cpu" |
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| | | # which gpu to train or finetune |
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| | | export CUDA_VISIBLE_DEVICES="0,1" |
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| | | gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') |
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| | | |
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| | | #--config-path "/Users/zhifu/funasr_github/examples/industrial_data_pretraining/paraformer-large/conf" \ |
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| | | #--config-name "finetune.yaml" \ |
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| | | # data dir, which contains: train.json, val.json |
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| | | data_dir="/Users/zhifu/funasr1.0/data/list" |
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| | | |
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| | | ## generate jsonl from wav.scp and text.txt |
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| | | #python -m funasr.datasets.audio_datasets.scp2jsonl \ |
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| | | #++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \ |
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| | | #++data_type_list='["source", "target"]' \ |
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| | | #++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl |
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| | | |
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| | | train_data="${data_dir}/train.jsonl" |
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| | | val_data="${data_dir}/val.jsonl" |
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| | | |
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| | | # exp output dir |
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| | | output_dir="/Users/zhifu/exp" |
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| | | log_file="${output_dir}/log.txt" |
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| | | |
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| | | mkdir -p ${output_dir} |
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| | | echo "log_file: ${log_file}" |
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| | | |
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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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