#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="0,1" # set gpus, e.g., CUDA_VISIBLE_DEVICES="0,1"
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gpu_num=2
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count=1
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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njob=4 # the number of jobs for each gpu
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train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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# general configuration
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feats_dir="../DATA" #feature output dictionary, for large data
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exp_dir="."
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lang=zh
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dumpdir=dump/fbank
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feats_type=fbank
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token_type=char
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scp=feats.scp
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type=kaldi_ark
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stage=1
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stop_stage=4
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# feature configuration
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feats_dim=560
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sample_frequency=16000
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nj=32
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speed_perturb="1.0"
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lfr=True
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lfr_m=7
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lfr_n=6
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init_model_name=speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning
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model_revision="v1.0.4" # please do not modify the model revision
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cmvn_file=init_model/${init_model_name}/am.mvn
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seg_file=init_model/${init_model_name}/seg_dict
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vocab=init_model/${init_model_name}/tokens.txt
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# data
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dataset= # dataset (include train/wav.scp, train/text, dev/wav.scp, dev/text, optional test/wav.scp test/text)
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# exp tag
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tag=""
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=train
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valid_set=dev
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test_sets="dev test"
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asr_config=conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
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init_param="init_model/${init_model_name}/model.pb"
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inference_config=conf/decode_asr_transformer_noctc_1best.yaml
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inference_asr_model=valid.acc.ave_10best.pth
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. utils/parse_options.sh || exit 1;
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# download model from modelscope
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python modelscope_utils/download_model.py --model_name ${init_model_name} --model_revision ${model_revision}
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if [ ! -d ${HOME}/.cache/modelscope/hub/damo/${init_model_name} ]; then
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echo "${HOME}/.cache/modelscope/hub/damo/${init_model_name} must exist"
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exit 1
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else
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if [ -d init_model/${init_model_name} ]; then
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echo "init_model/${init_model_name} is already exists. if you want to decode again, please delete init_model/${init_model_name} first."
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else
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mkdir -p init_model/${init_model_name}
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cp -r ${HOME}/.cache/modelscope/hub/damo/${init_model_name}/* init_model/${init_model_name}
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fi
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fi
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
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# you can set gpu num for decoding here
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gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
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ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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if ${gpu_inference}; then
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inference_nj=$[${ngpu}*${njob}]
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_ngpu=1
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else
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inference_nj=$njob
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_ngpu=0
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fi
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[ ! -d ${dataset} ] && echo "$0: Training data is required" && exit 1;
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[ ! -f ${dataset}/train/wav.scp ] && [ ! -f ${dataset}/train/text ] && echo "$0: Training data wav.scp or text is not found" && exit 1;
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if [ ! -d "${dataset}/dev" ]; then
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utils/fix_data.sh ${dataset}/train
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utils/subset_data_dir_tr_cv.sh --dev-num-utt 1000 ${dataset}/train ${dataset}
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fi
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if [ ! -d "${dataset}/test" ]; then
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test_sets="dev"
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fi
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feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
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feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
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feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature Generation"
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# compute fbank features
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fbankdir=${feats_dir}/fbank
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
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${dataset}/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
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utils/fix_data_feat.sh ${fbankdir}/train
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --sample_frequency ${sample_frequency} \
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${dataset}/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev
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utils/fix_data_feat.sh ${fbankdir}/dev
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if [ -d "${dataset}/test" ]; then
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utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --sample_frequency ${sample_frequency} \
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${dataset}/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test
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utils/fix_data_feat.sh ${fbankdir}/test
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fi
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echo "apply low_frame_rate and cmvn"
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[ ! -f ${cmvn_file} ] && echo "$0: cmvn file is required" && exit 1;
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utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
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--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
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${fbankdir}/train ${cmvn_file} ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
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utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
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--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
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${fbankdir}/dev ${cmvn_file} ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir}
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if [ -d "${dataset}/test" ]; then
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utils/apply_lfr_and_cmvn.sh --cmd "$train_cmd" --nj $nj \
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--lfr $lfr --lfr-m $lfr_m --lfr-n $lfr_n \
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${fbankdir}/test ${cmvn_file} ${exp_dir}/exp/make_fbank/test ${feat_test_dir}
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fi
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echo "Text Tokenize"
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# 我爱reading->我 爱 read@@ ing
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utils/text_tokenize.sh --cmd "$train_cmd" --nj $nj ${fbankdir}/train ${seg_file} ${feat_train_dir}/log ${feat_train_dir}
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utils/fix_data_feat.sh ${feat_train_dir}
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utils/text_tokenize.sh --cmd "$train_cmd" --nj $nj ${fbankdir}/dev ${seg_file} ${feat_dev_dir}/log ${feat_dev_dir}
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utils/fix_data_feat.sh ${feat_dev_dir}
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if [ -d "${dataset}/test" ]; then
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cp ${fbankdir}/test/text ${feat_test_dir}
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fi
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fi
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token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
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echo "dictionary: ${token_list}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p ${feats_dir}/data/${lang}_token_list/char/
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cp $vocab ${token_list}
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vocab_size=$(wc -l <${token_list})
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awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
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awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/train
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mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev
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cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/train
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cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev
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fi
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# Training Stage
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Training"
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# update asr train config.yaml
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python modelscope_utils/update_config.py --modelscope_config init_model/${init_model_name}/finetune.yaml --finetune_config ${asr_config} --output_config init_model/${init_model_name}/asr_finetune_config.yaml
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finetune_config=init_model/${init_model_name}/asr_finetune_config.yaml
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mkdir -p ${exp_dir}/exp/${model_dir}
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mkdir -p ${exp_dir}/exp/${model_dir}/log
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INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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asr_train_paraformer.py \
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--gpu_id $gpu_id \
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--use_preprocessor true \
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--token_type $token_type \
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--token_list $token_list \
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--train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
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--train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
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--train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
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--train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
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--valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
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--valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
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--valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
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--valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \
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--resume true \
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--output_dir ${exp_dir}/exp/${model_dir} \
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--init_param $init_param \
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--config $finetune_config \
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--input_size $feats_dim \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--multiprocessing_distributed true \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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fi
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# Testing Stage
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# Testing Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: Inference"
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for dset in ${test_sets}; do
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asr_exp=${exp_dir}/exp/${model_dir}
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inference_tag="$(basename "${inference_config}" .yaml)"
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_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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_logdir="${_dir}/logdir"
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if [ -d ${_dir} ]; then
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echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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exit 0
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fi
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mkdir -p "${_logdir}"
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_data="${feats_dir}/${dumpdir}/${dset}"
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key_file=${_data}/${scp}
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num_scp_file="$(<${key_file} wc -l)"
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_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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split_scps=
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for n in $(seq "${_nj}"); do
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split_scps+=" ${_logdir}/keys.${n}.scp"
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done
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# shellcheck disable=SC2086
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utils/split_scp.pl "${key_file}" ${split_scps}
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_opts=
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if [ -n "${inference_config}" ]; then
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_opts+="--config ${inference_config} "
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fi
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${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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python -m funasr.bin.asr_inference_launch \
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--batch_size 1 \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--gpuid_list ${gpuid_list} \
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--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${asr_exp}"/config.yaml \
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--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode paraformer \
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${_opts}
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for f in token token_int score text; do
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if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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for i in $(seq "${_nj}"); do
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cat "${_logdir}/output.${i}/1best_recog/${f}"
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done | sort -k1 >"${_dir}/${f}"
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fi
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done
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python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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python utils/proce_text.py ${_data}/text ${_data}/text.proc
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python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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cat ${_dir}/text.cer.txt
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done
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fi
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