| | |
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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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| | | |
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| | | # general configuration |
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| | | feats_dir="../DATA" #feature output dictionary, for large data |
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| | |
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| | | lfr_n=6 |
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| | | |
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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="v1.0.3" # please do not modify the model revision |
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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/${init_model_name}/tokens.txt |
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| | |
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| | | # you can set gpu num for decoding here |
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| | | gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default |
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| | | ngpu=$(echo $gpuid_list | awk -F "," '{print NF}') |
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| | | inference_nj=$[${ngpu}*${njob}] |
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| | | |
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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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| | | |
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| | | if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then |
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| | | echo "stage 0: Data preparation" |
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| | |
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| | | feat_train_dir=${feats_dir}/${dumpdir}/${train_set}; mkdir -p ${feat_train_dir} |
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| | | feat_dev_dir=${feats_dir}/${dumpdir}/${valid_set}; mkdir -p ${feat_dev_dir} |
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| | | if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then |
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| | | echo "Feature Generation" |
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| | | echo "stage 1: Feature Generation" |
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| | | # compute fbank features |
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| | | fbankdir=${feats_dir}/fbank |
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| | | utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \ |
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| | |
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| | | # Training Stage |
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| | | world_size=$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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| | |
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| | | |
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| | | # Testing Stage |
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| | | if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then |
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| | | ./utils/easy_asr_infer.sh \ |
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| | | --lang zh \ |
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| | | --datadir ${feats_dir} \ |
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| | | --feats_type ${feats_type} \ |
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| | | --feats_dim ${feats_dim} \ |
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| | | --token_type ${token_type} \ |
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| | | --gpu_inference ${gpu_inference} \ |
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| | | --inference_config "${inference_config}" \ |
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| | | --test_sets "${test_sets}" \ |
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| | | --token_list $token_list \ |
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| | | --asr_exp ${exp_dir}/exp/${model_dir} \ |
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| | | --stage 12 \ |
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| | | --stop_stage 12 \ |
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| | | --scp $scp \ |
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| | | --text text \ |
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| | | --inference_nj $inference_nj \ |
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| | | --njob $njob \ |
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| | | --inference_asr_model $inference_asr_model \ |
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| | | --gpuid_list $gpuid_list \ |
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| | | --mode paraformer |
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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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| | | ${_opts} |
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| | | |
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| | | for f in token token_int score text; do |
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| | | if [ -f "${_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 |
|---|
| | | fi |
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| | | |
|---|
