#!/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="4,5,6,7"
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gpu_num=4
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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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finetune=true
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=2
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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
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exp_dir="."
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lang=zh
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token_type=char
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type=sound
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scp=wav.scp
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speed_perturb="1.0"
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stage=0
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stop_stage=1
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# feature configuration
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feats_dim=80
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nj=64
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# exp tag
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tag="finetune"
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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_Ali_far_wpegss
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valid_set=Test_Ali_far_wpegss
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test_sets="${DATA_NAME}_wpegss"
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asr_config=conf/train_paraformer.yaml
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model_dir="$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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pretrain_model_dir=./speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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inference_config=$pretrain_model_dir/decoding.yaml
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token_list=$pretrain_model_dir/tokens.txt
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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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if ${finetune}; then
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inference_asr_model=./checkpoint/valid.acc.ave.pb
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finetune_tag="_finetune"
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else
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inference_asr_model=$pretrain_model_dir/model.pb
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finetune_tag=""
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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if [ -L ./utils ]; then
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unlink ./utils
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ln -s ../../aishell/transformer/utils
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else
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ln -s ../../aishell/transformer/utils
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fi
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fi
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# Download Model
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Download Model"
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if [ ! -d $pretrain_model_dir ]; then
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git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git
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fi
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fi
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# ASR Training Stage
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: ASR Training"
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python -m torch.distributed.launch \
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--nproc_per_node $gpu_num local/finetune.py
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fi
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# Testing Stage
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Inference"
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for dset in ${test_sets}; do
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_dir="$pretrain_model_dir/decode_${dset}${finetune_tag}"
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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 "${_logdir}"
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_data="./data/${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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--cmvn_file $pretrain_model_dir/am.mvn \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config $pretrain_model_dir/config.yaml \
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--asr_model_file $inference_asr_model \
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--output_dir "${_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 local/merge_spk_text.py ${_dir}/text ${_data}/utt2spk
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python local/compute_cpcer.py ${_data}/text_merge ${_dir}/text_merge
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echo "cpCER is saved at ${_dir}/text_cpcer"
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done
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fi
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