#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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gpu_devices="6,7"
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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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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=1
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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="." #feature output dictionary
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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=spk
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scp=feats.scp
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type=kaldi_ark
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stage=0
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stop_stage=4
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# feature configuration
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feats_dim=80
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sample_frequency=16000
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nj=32
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speed_perturb="0.9,1.0,1.1"
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# data
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data_cnceleb=
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# exp tag
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tag=""
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inference_tag="basic"
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inference_sv_model=sv.pb
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sv_config=sv.yaml
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. utils/parse_options.sh || exit 1;
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model_dir="baseline_$(basename "${sv_config}" .yaml)_${feats_type}_${lang}_${token_type}_${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="eval_enroll eval_test"
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# you can set gpu num for decoding here
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gpuid_list=$gpu_devices # set gpus for decoding, the same as training stage by default
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ngpu=$(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 [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# Data preparation
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bash local/make_cnceleb1.sh ${data_cnceleb}/CN-Celeb1 ${feats_dir}/data
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# bash local/make_cnceleb2.sh ${data_cnceleb}/CN-Celeb2 ${feats_dir}/data/cnceleb2_train
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grep speech ${feats_dir}/data/eval_test/trials/trials.lst > ${feats_dir}/data/eval_test/trials/trials.lst.speech
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# local/combine_data.sh ${feats_dir}/data/train ${feats_dir}/data/cnceleb1_train ${feats_dir}/data/cnceleb2_train
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fi
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# Testing Stage
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Inference"
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for dset in ${test_sets}; do
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echo "extracting embedding for ${dset}"
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asr_exp=${exp_dir}/exp/${model_dir}
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_dir="${asr_exp}/${inference_tag}/${inference_sv_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 "${_logdir}"
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_data="data/${dset}"
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key_file=${_data}/wav.scp
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num_scp_file="$(<${key_file} wc -l)"
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_nj=$([ $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}" ${split_scps}
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${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/sv_inference.JOB.log \
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python -m funasr.bin.sv_inference \
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--gpuid_list ${gpu_devices} \
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--ngpu ${_ngpu} \
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--key_file "${_logdir}"/keys.JOB.scp \
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--data_path_and_name_and_type data/${dset}/wav.scp,speech,sound \
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--allow_variable_data_keys true \
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--sv_train_config ${sv_config} \
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--sv_model_file ${inference_sv_model} \
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--output_dir "${_logdir}"/output.JOB \
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--num_workers 1
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for f in xvector.ark; do
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if [ -f "${_logdir}/output.1/${f}" ]; then
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for i in $(seq "${_nj}"); do
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echo "${_logdir}/output.${i}/${f}"
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done > "${_dir}/${f}.flist"
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fi
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done
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done
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# compute eer and minDCF results
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echo "stage 2: computing EER and minDCF"
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asr_exp=${exp_dir}/exp/${model_dir}
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_dir="${asr_exp}/${inference_tag}/${inference_sv_model}"
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mkdir -p ${_dir}/score
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cp ${_dir}/eval_enroll/xvector.ark.flist ${_dir}/score/spk2xvec.flist
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cp ${_dir}/eval_test/xvector.ark.flist ${_dir}/score/utt2xvec.flist
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cp ${feats_dir}/data/eval_test/trials/trials.lst.speech ${_dir}/score/trials
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python sid/calc_trial_scores.py --no_pbar ${_dir}/score ${_dir}/score/trials ${_dir}/score/trials.cos
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python sid/compute_eer.py ${_dir}/score/trials ${_dir}/score/trials.cos
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python sid/compute_min_dcf.py --c-miss 10 --p-target 0.01 \
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${_dir}/score/trials.cos ${_dir}/score/trials 2>/dev/null
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fi
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