#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# This recipe aims at reimplement the results of SOND on Callhome corpus which is represented in
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# [1] TOLD: A Novel Two-stage Overlap-aware Framework for Speaker Diarization, ICASSP 2023
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# You can also use it on other dataset such AliMeeting to reproduce the results in
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# [2] Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis, EMNLP 2022
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# We recommend you run this script stage by stage.
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# This recipe includes:
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# 1. simulating data with switchboard and NIST.
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# 2. training the model from scratch for 3 stages:
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# 2-1. pre-train on simu_swbd_sre
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# 2-2. train on simu_swbd_sre
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# 2-3. finetune on callhome1
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# 3. evaluating model with the results from the first stage EEND-OLA,
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# Finally, you will get a similar DER result claimed in the paper.
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# environment configuration
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# path/to/kaldi
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kaldi_root=
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if [ -z "${kaldi_root}" ]; then
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echo "We need kaldi to prepare dataset, extract fbank features, please install kaldi first and set kaldi_root."
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echo "Kaldi installation guide can be found at https://kaldi-asr.org/"
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exit;
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fi
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if [ ! -e local ]; then
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ln -s ${kaldi_root}/egs/callhome_diarization/v2/local ./local
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fi
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if [ ! -e utils ]; then
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ln -s ${kaldi_root}/egs/callhome_diarization/v2/utils ./utils
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fi
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# path to Switchboard and NIST including:
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# LDC98S75, LDC99S79, LDC2002S06, LDC2001S13, LDC2004S07
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data_root=
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if [ -z "${data_root}" ]; then
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echo "We need Switchboard and NIST to simulate data for pretraining."
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echo "If you can't get them, please use 'finetune.sh' to finetune a pretrained model."
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exit;
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fi
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# path/to/NIST/LDC2001S97
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callhome_root=
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if [ -z "${callhome_root}" ]; then
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echo "We need callhome corpus for training."
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echo "If you want inference only, please refer https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary"
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exit;
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fi
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# machines configuration
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gpu_devices="4,5,6,7" # for V100-16G, use 4 GPUs
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gpu_num=4
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count=1
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# general configuration
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stage=0
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stop_stage=19
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# number of jobs for data process
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nj=16
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sr=8000
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# experiment configuration
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lang=en
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feats_type=fbank
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datadir=data
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dumpdir=dump
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expdir=exp
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train_cmd=utils/run.pl
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# training related
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tag=""
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train_set=simu_swbd_sre
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valid_set=callhome1
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train_config=conf/EAND_ResNet34_SAN_L4N512_None_FFN_FSMN_L6N512_bce_dia_loss_01.yaml
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token_list=${datadir}/token_list/powerset_label_n16k4.txt
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init_param=
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freeze_param=
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# inference related
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inference_model=valid.der.ave_5best.pb
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inference_config=conf/basic_inference.yaml
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inference_tag=""
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test_sets="callhome2"
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# number of jobs for inference
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=4
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infer_cmd=utils/run.pl
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told_max_iter=4
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. utils/parse_options.sh || exit 1;
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model_dir="$(basename "${train_config}" .yaml)_${feats_type}_${lang}${tag}"
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# you can set gpu num for decoding here
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gpuid_list=$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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# Prepare datasets
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# 1. Prepare a collection of NIST SRE data.
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echp "Stage 0: Prepare a collection of NIST SRE data."
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local/make_sre.sh $data_root ${datadir}
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# 2.a Prepare SWB.
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local/make_swbd2_phase1.pl ${data_root}/LDC98S75 \
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${datadir}/swbd2_phase1_train
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local/make_swbd2_phase2.pl $data_root/LDC99S79 \
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${datadir}/swbd2_phase2_train
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local/make_swbd2_phase3.pl $data_root/LDC2002S06 \
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${datadir}/swbd2_phase3_train
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local/make_swbd_cellular1.pl $data_root/LDC2001S13 \
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${datadir}/swbd_cellular1_train
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local/make_swbd_cellular2.pl $data_root/LDC2004S07 \
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${datadir}/swbd_cellular2_train
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# 2.b combine all swbd data.
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utils/combine_data.sh ${datadir}/swbd \
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${datadir}/swbd2_phase1_train ${datadir}/swbd2_phase2_train ${datadir}/swbd2_phase3_train \
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${datadir}/swbd_cellular1_train ${datadir}/swbd_cellular2_train
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utils/validate_data_dir.sh --no-text --no-feats ${datadir}/swbd
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utils/fix_data_dir.sh ${datadir}/swbd
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utils/combine_data.sh ${datadir}/swbd_sre ${datadir}/swbd ${datadir}/sre
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utils/validate_data_dir.sh --no-text --no-feats ${datadir}/swbd_sre
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utils/fix_data_dir.sh ${datadir}/swbd_sre
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# 3. Prepare the Callhome portion of NIST SRE 2000.
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local/make_callhome.sh ${callhome_root} ${datadir}/
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# 4. split ref.rttm
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for dset in callhome1 callhome2; do
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rm -rf ${datadir}/${dset}/ref.rttm
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for name in `awk '{print $1}' ${datadir}/${dset}/wav.scp`; do
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grep ${name} ${datadir}/callhome/fullref.rttm >> ${datadir}/${dset}/ref.rttm;
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done
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# filter out records which don't have rttm labels.
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awk '{print $2}' ${datadir}/${dset}/ref.rttm | sort | uniq > ${datadir}/${dset}/uttid
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mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/wav.scp.bak
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awk '{if (NR==FNR){a[$1]=1}else{if (a[$1]==1){print $0}}}' ${datadir}/${dset}/uttid ${datadir}/${dset}/wav.scp.bak > ${datadir}/${dset}/wav.scp
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mkdir ${datadir}/${dset}/raw
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mv ${datadir}/${dset}/{reco2num_spk,segments,spk2utt,utt2spk,uttid,wav.scp.bak} ${datadir}/${dset}/raw/
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awk '{print $1,$1}' ${datadir}/${dset}/wav.scp > ${datadir}/${dset}/utt2spk
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done
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "Stage 1: Dump sph file to wav"
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export PATH=${kaldi_root}/tools/sph2pipe/:${PATH}
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if [ ! -f ${kaldi_root}/tools/sph2pipe/sph2pipe ]; then
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echo "Can not find sph2pipe in ${kaldi_root}/tools/sph2pipe/,"
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echo "please install sph2pipe and put it in the right place."
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exit;
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fi
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for dset in callhome1 callhome2 swbd_sre; do
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echo "Stage 1: start to dump ${dset}."
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mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/sph.scp
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mkdir -p ${dumpdir}/${dset}/wavs
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python -Wignore script/dump_pipe_wav.py ${datadir}/${dset}/sph.scp ${dumpdir}/${dset}/wavs \
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--sr ${sr} --nj ${nj} --no_pbar
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find `pwd`/${dumpdir}/${dset}/wavs -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/wav.scp
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done
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "Stage 2: Extract non-overlap segments from callhome dataset"
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for dset in callhome1 callhome2; do
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echo "Stage 2: Extracting non-overlap segments for "${dset}
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mkdir -p ${dumpdir}/${dset}/nonoverlap_0s
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python -Wignore script/extract_nonoverlap_segments.py \
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${datadir}/${dset}/wav.scp ${datadir}/${dset}/ref.rttm ${dumpdir}/${dset}/nonoverlap_0s \
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--min_dur 0.1 --max_spk_num 8 --sr ${sr} --no_pbar --nj ${nj}
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mkdir -p ${datadir}/${dset}/nonoverlap_0s
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find ${dumpdir}/${dset}/nonoverlap_0s/ -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/nonoverlap_0s/wav.scp
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awk -F'[/.]' '{print $(NF-1),$(NF-2)}' ${datadir}/${dset}/nonoverlap_0s/wav.scp > ${datadir}/${dset}/nonoverlap_0s/utt2spk
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echo "Done."
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done
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "Stage 3: Generate concatenated waveforms for each speaker in switchboard, sre and callhome1"
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mkdir swb_sre_resources
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wget --no-check-certificate -P swb_sre_resources/ https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/Speaker_Diar/swb_sre_resources/noise.scp
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wget --no-check-certificate -P swb_sre_resources/ https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/Speaker_Diar/swb_sre_resources/swbd_sre_tdnn_vad_segments
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mkdir ${datadir}/swbd_sre/none_silence
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ln -s swb_sre_resources/swbd_sre_tdnn_vad_segments ${datadir}/swbd_sre/none_silence/segments
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cp ${datadir}/swbd_sre/wav.scp ${datadir}/swbd_sre/none_silence/reco.scp
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mkdir -p ${dumpdir}/swbd_sre/none_silence
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python -Wignore script/remove_silence_from_wav.py \
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${datadir}/swbd_sre/none_silence ${dumpdir}/swbd_sre/none_silence --nj ${nj} --sr 8000
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# The utterance number in wav.scp may be different from reco.scp,
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# since some recordings don't appear in the segments file, may due to the VAD
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echo "find wavs_nosil"
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find `pwd`/${dumpdir}/swbd_sre/none_silence -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/swbd_sre/none_silence/wav.scp
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echo "concat spk segments"
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ln -s ${datadir}/swbd_sre/utt2spk ${datadir}/swbd_sre/none_silence/utt2spk
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echo "Stage 3: Start to concatnate waveforms for speakers in switchboard and sre"
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python -Wignore egs/callhome/concat_spk_segs.py \
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${datadir}/swbd_sre/none_silence ${dumpdir}/swbd_sre/spk_wavs --nj ${nj} --sr 8000
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echo "Stage 3: Start to concatnate waveforms for speakers in callhome1"
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# only use callhome1 as training set to simulate data
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python -Wignore egs/callhome/concat_spk_segs.py \
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${datadir}/callhome1/nonoverlap_0s ${dumpdir}/callhome1/spk_wavs --nj ${nj} --sr 8000
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fi
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# simulate data with the pattern of callhome1
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "Stage 4: Start to simulate recordings."
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if [ ! -e ${dumpdir}/musan ]; then
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echo "Stage 4-1: Start to download MUSAN noises from openslr"
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wget --no-check-certificate -P ${dumpdir}/musan https://www.openslr.org/resources/17/musan.tar.gz
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tar -C ${dumpdir}/musan -xvf ${dumpdir}/musan/musan.tar.gz
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fi
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if [ ! -e ${dumpdir}/rirs ]; then
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echo "Stage 4-2: Start to download RIRs from openslr"
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wget --no-check-certificate -P ${dumpdir}/rirs https://www.openslr.org/resources/28/rirs_noises.zip
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unzip ${dumpdir}/rirs/rirs_noises.zip -d ${dumpdir}/rirs
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fi
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mkdir -p ${datadir}/simu_swbd_sre
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# only use background noises instead of all noises in MUSAN.
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sed "s:/path/to/musan/:`pwd`/${dumpdir}/musan/:g" swb_sre_resources/noise.scp > ${datadir}/simu_swbd_sre/noise.scp
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# use simulated RIRs.
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find `pwd`/${dumpdir}/rirs/RIRS_NOISES/simulated_rirs/ -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-3)"-"$(NF-1), $0}' > ${datadir}/simu_swbd_sre/rirs.scp
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cp ${datadir}/callhome1/{ref.rttm,reco2num_spk} ${datadir}/simu_swbd_sre
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find `pwd`/${dumpdir}/swbd_sre/spk_wavs -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/spk2wav.scp
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echo "Stage 4-3: Start to simulate recordings with variable speakers as Callhome1 patterns."
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# average duration of callhome is 125s, about 20 chunk with silence
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# simulating 22500 (45 jobs x 500 reco) recordings, without random_assign and random_shift_interval
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for i in $(seq 0 44); do
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cmd="python -Wignore egs/callhome/simu_whole_recordings.py \
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${datadir}/simu_swbd_sre \
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${dumpdir}/simu_swbd_sre/wavs \
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--corpus_name simu_swbd_sre --task_id $i --total_mix 500 --sr 8000 --no_bar &"
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echo $cmd
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eval $cmd
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done
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wait;
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echo "Stage 4-4: Start to simulate recordings with fixed speakers as Callhome1 patterns."
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# simulating 30000 (30 jobs x 1000 reco) recordings for different speaker number 2, 3, 4
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for n_spk in $(seq 2 4); do
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mkdir -p /home/neo.dzh/corpus/simu_swbd_sre/${n_spk}spk_wavs
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for i in $(seq 0 29); do
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cmd="python -Wignore egs/callhome/simu_whole_recordings.py \
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${datadir}/simu_swbd_sre \
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${dumpdir}/simu_swbd_sre/${n_spk}spk_wavs \
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--random_assign_spk --random_interval --spk_num ${n_spk} \
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--corpus_name simu_swbd_sre --task_id $i --total_mix 1000 --sr 8000 --no_bar &"
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echo $cmd
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eval $cmd
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done
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wait;
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done
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find `pwd`/${dumpdir}/simu_swbd_sre -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/wav.scp
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awk '{print $1,$1}' ${datadir}/simu_swbd_sre/wav.scp > ${datadir}/simu_swbd_sre/utt2spk
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find `pwd`/${dumpdir}/simu_swbd_sre -iname "*.rttm" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/rttm.scp
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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echo "Stage 5: Generate fbank features"
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home_path=`pwd`
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cd ${kaldi_root}/egs/callhome_diarization/v2 || exit
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. ./cmd.sh
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. ./path.sh
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for dset in simu_swbd_sre callhome1 callhome2; do
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steps/make_fbank.sh --write-utt2num-frames true --fbank-config conf/fbank.conf --nj ${nj} --cmd "$train_cmd" \
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${datadir}/${dset} ${expdir}/make_fbank/${dset} ${dumpdir}/${dset}/fbank
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utils/fix_data_dir.sh ${datadir}/${dset}
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done
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for dset in swbd_sre/none_silence callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
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steps/make_fbank.sh --write-utt2num-frames true --fbank-config conf/fbank.conf --nj ${nj} --cmd "$train_cmd" \
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${datadir}/${dset} ${expdir}/make_fbank/${dset} ${dumpdir}/${dset}/fbank
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utils/fix_data_dir.sh ${datadir}/${dset}
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done
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cd ${home_path} || exit
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fi
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if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
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echo "Stage 6: Extract speaker embeddings."
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sv_exp_dir=exp/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
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if [ ! -e ${sv_exp_dir} ]; then
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echo "start to download sv models"
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git lfs install
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git clone https://www.modelscope.cn/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch.git
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mv speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ${expdir}/
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echo "Done."
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fi
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sed "s/input_size: null/input_size: 80/g" ${sv_exp_dir}/sv.yaml > ${sv_exp_dir}/sv_fbank.yaml
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for dset in swbd_sre/none_silence callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
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key_file=${datadir}/${dset}/feats.scp
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num_scp_file="$(<${key_file} wc -l)"
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_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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_logdir=${dumpdir}/${dset}/xvecs
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mkdir -p ${_logdir}
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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_launch \
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--batch_size 1 \
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--njob ${njob} \
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--ngpu "${_ngpu}" \
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--gpuid_list ${gpuid_list} \
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--data_path_and_name_and_type "${key_file},speech,kaldi_ark" \
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--key_file "${_logdir}"/keys.JOB.scp \
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--sv_train_config ${sv_exp_dir}/sv_fbank.yaml \
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--sv_model_file ${sv_exp_dir}/sv.pth \
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--output_dir "${_logdir}"/output.JOB
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cat ${_logdir}/output.*/xvector.scp | sort > ${datadir}/${dset}/utt2xvec
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done
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fi
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if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
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echo "Stage 7: Generate label files."
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for dset in simu_swbd_sre callhome1 callhome2; do
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echo "Stage 7: Generate labels for ${dset}."
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python -Wignore script/calc_real_meeting_frame_labels.py \
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${datadir}/${dset} ${dumpdir}/${dset}/labels \
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--n_spk 8 --frame_shift 0.01 --nj 16 --sr 8000
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find `pwd`/${dumpdir}/${dset}/labels/ -iname "*.lbl.mat" | awk -F'[/.]' '{print $(NF-2),$0}' | sort > ${datadir}/${dset}/labels.scp
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done
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fi
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if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
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echo "Stage 8: Make training and evaluation files."
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# dump simulated data in training mode (randomly shuffle the speaker order).
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data_dir=${datadir}/simu_swbd_sre/files_for_dump
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mkdir ${data_dir}
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cp ${datadir}/simu_swbd_sre/{feats.scp,labels.scp} ${data_dir}/
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cp ${datadir}/swbd_sre/none_silence/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
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# dump data with the window length of 1600 frames and hop length of 400 frames.
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echo "Stage 8: start to dump for simu_swbd_sre."
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for i in $(seq 0 49); do
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cmd="python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
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--out ${dumpdir}/simu_swbd_sre/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode train \
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--chunk_size 1600 --chunk_shift 400 \
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--task_id ${i} --task_size 2250 &"
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echo $cmd
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eval $cmd
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done
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wait;
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mkdir -p ${datadir}/simu_swbd_sre/dumped_files
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cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_feat.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/feats.scp
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cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/profile.scp
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cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_label.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/label.scp
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mkdir -p ${expdir}/simu_swbd_sre_states
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awk '{print $1,"1600"}' ${datadir}/simu_swbd_sre/dumped_files/feats.scp | shuf > ${expdir}/simu_swbd_sre_states/speech_shape
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# dump callhome1 data in training mode.
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data_dir=${datadir}/callhome1/files_for_dump
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mkdir ${data_dir}
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# filter out zero duration segments
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LC_ALL=C awk '{if ($5 > 0){print $0}}' ${datadir}/callhome1/ref.rttm > ${data_dir}/ref.rttm
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cp ${datadir}/callhome1/{feats.scp,labels.scp} ${data_dir}/
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cp ${datadir}/callhome1/nonoverlap_0s/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
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echo "Stage 8: start to dump for callhome1."
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python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
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--out ${dumpdir}/callhome1/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode train \
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--chunk_size 1600 --chunk_shift 400 --add_mid_to_speaker true
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mkdir -p ${datadir}/callhome1/dumped_files
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cat ${dumpdir}/callhome1/dumped_files/data_parts*_feat.scp | sort > ${datadir}/callhome1/dumped_files/feats.scp
|
cat ${dumpdir}/callhome1/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/callhome1/dumped_files/profile.scp
|
cat ${dumpdir}/callhome1/dumped_files/data_parts*_label.scp | sort > ${datadir}/callhome1/dumped_files/label.scp
|
mkdir -p ${expdir}/callhome1_states
|
awk '{print $1,"1600"}' ${datadir}/callhome1/dumped_files/feats.scp | shuf > ${expdir}/callhome1_states/speech_shape
|
python -Wignore script/convert_rttm_to_seg_file.py --rttm_scp ${data_dir}/ref.rttm --seg_file ${data_dir}/org_vad.txt
|
|
# dump callhome2 data in test mode.
|
data_dir=${datadir}/callhome2/files_for_dump
|
mkdir ${data_dir}
|
# filter out zero duration segments
|
LC_ALL=C awk '{if ($5 > 0){print $0}}' ${datadir}/callhome2/ref.rttm > ${data_dir}/ref.rttm
|
cp ${datadir}/callhome2/{feats.scp,labels.scp} ${data_dir}/
|
cp ${datadir}/callhome2/nonoverlap_0s/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
|
|
echo "Stage 8: start to dump for callhome2."
|
python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
|
--out ${dumpdir}/callhome2/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode test \
|
--chunk_size 1600 --chunk_shift 400 --add_mid_to_speaker true
|
|
mkdir -p ${datadir}/callhome2/dumped_files
|
cat ${dumpdir}/callhome2/dumped_files/data_parts*_feat.scp | sort > ${datadir}/callhome2/dumped_files/feats.scp
|
cat ${dumpdir}/callhome2/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/callhome2/dumped_files/profile.scp
|
cat ${dumpdir}/callhome2/dumped_files/data_parts*_label.scp | sort > ${datadir}/callhome2/dumped_files/label.scp
|
mkdir -p ${expdir}/callhome2_states
|
awk '{print $1,"1600"}' ${datadir}/callhome2/dumped_files/feats.scp | shuf > ${expdir}/callhome2_states/speech_shape
|
python -Wignore script/convert_rttm_to_seg_file.py --rttm_scp ${data_dir}/ref.rttm --seg_file ${data_dir}/org_vad.txt
|
|
fi
|
|
# Training Stage, phase 1, pretraining on simulated data with frozen encoder parameters.
|
# This training may cost about 1.8 days.
|
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
|
echo "stage 10: training phase 1, pretraining on simulated data"
|
world_size=$gpu_num # run on one machine
|
mkdir -p ${expdir}/${model_dir}
|
mkdir -p ${expdir}/${model_dir}/log
|
mkdir -p /tmp/${model_dir}
|
INIT_FILE=/tmp/${model_dir}/ddp_init
|
if [ -f $INIT_FILE ];then
|
rm -f $INIT_FILE
|
fi
|
init_opt=""
|
if [ ! -z "${init_param}" ]; then
|
init_opt="--init_param ${init_param}"
|
echo ${init_opt}
|
fi
|
|
freeze_opt=""
|
if [ ! -z "${freeze_param}" ]; then
|
freeze_opt="--freeze_param ${freeze_param}"
|
echo ${freeze_opt}
|
fi
|
|
init_method=file://$(readlink -f $INIT_FILE)
|
echo "$0: init method is $init_method"
|
for ((i = 0; i < $gpu_num; ++i)); do
|
{
|
rank=$i
|
local_rank=$i
|
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
|
python -m funasr.bin.diar_train \
|
--gpu_id $gpu_id \
|
--use_preprocessor false \
|
--token_type char \
|
--token_list $token_list \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--train_shape_file ${expdir}/${train_set}_states/speech_shape \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
|
--init_param ${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/sv.pth:encoder:encoder \
|
--unused_parameters true \
|
--freeze_param encoder \
|
${init_opt} \
|
${freeze_opt} \
|
--ignore_init_mismatch true \
|
--resume true \
|
--output_dir ${expdir}/${model_dir} \
|
--config $train_config \
|
--ngpu $gpu_num \
|
--num_worker_count $count \
|
--multiprocessing_distributed true \
|
--dist_init_method $init_method \
|
--dist_world_size $world_size \
|
--dist_rank $rank \
|
--local_rank $local_rank 1> ${expdir}/${model_dir}/log/train.log.$i 2>&1
|
} &
|
done
|
echo "Training log can be found at ${expdir}/${model_dir}/log/train.log.*"
|
wait
|
fi
|
|
# evaluate for pretrained model
|
if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
|
echo "stage 11: evaluation for phase-1 model."
|
for dset in ${test_sets}; do
|
echo "Processing for $dset"
|
exp_model_dir=${expdir}/${model_dir}
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
if [ -d ${_dir} ]; then
|
echo "WARNING: ${_dir} is already exists."
|
fi
|
mkdir -p "${_logdir}"
|
_data="${datadir}/${dset}/dumped_files"
|
key_file=${_data}/feats.scp
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
_opt=
|
if [ ! -z "${inference_config}" ]; then
|
_opt="--config ${inference_config}"
|
fi
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
echo "Inference log can be found at ${_logdir}/inference.*.log"
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
|
python -m funasr.bin.diar_inference_launch \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob} \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
|
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--diar_train_config "${exp_model_dir}"/config.yaml \
|
--diar_model_file "${exp_model_dir}"/"${inference_model}" \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode sond ${_opt}
|
done
|
fi
|
|
# Scoring for pretrained model, you may get a DER like 13.29 16.54
|
# 13.29: with oracle VAD, 16.54: with only SOND outputs, aka, system VAD.
|
if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
|
echo "stage 12: Scoring phase-1 models"
|
if [ ! -e dscore ]; then
|
git clone https://github.com/nryant/dscore.git
|
# add intervaltree to setup.py
|
fi
|
for dset in ${test_sets}; do
|
echo "stage 12: Scoring for ${dset}"
|
diar_exp=${expdir}/${model_dir}
|
_data="${datadir}/${dset}"
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
|
|
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
|
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
|
# echo ${cmd}
|
eval ${cmd}
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.ref_vad
|
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.sys_vad
|
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
|
done
|
fi
|
|
# Training Stage, phase 2, training on simulated data without frozen parameters.
|
# For V100-16G, please set batch_size to 8 in the config, and use 4 GPU to train the model with options like --gpu_devices 4,5,6,7 --gpu_num 4.
|
# For V100-32G, please set batch_size to 16 in the config, and use 2 GPU to train the model with options like --gpu_devices 4,5,6,7 --gpu_num 2.
|
# This training may cost about 3.5 days.
|
if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
|
echo "stage 13: training phase 2, training on simulated data"
|
world_size=$gpu_num # run on one machine
|
mkdir -p ${expdir}/${model_dir}_phase2
|
mkdir -p ${expdir}/${model_dir}_phase2/log
|
mkdir -p /tmp/${model_dir}_phase2
|
INIT_FILE=/tmp/${model_dir}_phase2/ddp_init
|
if [ -f $INIT_FILE ];then
|
rm -f $INIT_FILE
|
fi
|
init_opt=""
|
if [ ! -z "${init_param}" ]; then
|
init_opt="--init_param ${init_param}"
|
echo ${init_opt}
|
fi
|
|
freeze_opt=""
|
if [ ! -z "${freeze_param}" ]; then
|
freeze_opt="--freeze_param ${freeze_param}"
|
echo ${freeze_opt}
|
fi
|
|
phase2_config="$(dirname "${train_config}")/$(basename "${train_config}" .yaml)_phase2.yaml"
|
|
init_method=file://$(readlink -f $INIT_FILE)
|
echo "$0: init method is $init_method"
|
for ((i = 0; i < $gpu_num; ++i)); do
|
{
|
rank=$i
|
local_rank=$i
|
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
|
python -m funasr.bin.diar_train \
|
--gpu_id $gpu_id \
|
--use_preprocessor false \
|
--token_type char \
|
--token_list $token_list \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--train_shape_file ${expdir}/${train_set}_states/speech_shape \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
|
--init_param exp/${model_dir}/valid.der.ave_5best.pb \
|
--unused_parameters true \
|
${init_opt} \
|
${freeze_opt} \
|
--ignore_init_mismatch true \
|
--resume true \
|
--output_dir ${expdir}/${model_dir}_phase2 \
|
--config ${phase2_config} \
|
--ngpu $gpu_num \
|
--num_worker_count $count \
|
--multiprocessing_distributed true \
|
--dist_init_method $init_method \
|
--dist_world_size $world_size \
|
--dist_rank $rank \
|
--local_rank $local_rank 1> ${expdir}/${model_dir}_phase2/log/train.log.$i 2>&1
|
} &
|
done
|
echo "Training log can be found at ${expdir}/${model_dir}_phase2/log/train.log.*"
|
wait
|
fi
|
|
# evaluate for phase-2 model
|
if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
|
echo "stage 14: evaluation for phase-2 model ${inference_model}."
|
for dset in ${test_sets}; do
|
echo "Processing for $dset"
|
exp_model_dir=${expdir}/${model_dir}_phase2
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
if [ -d ${_dir} ]; then
|
echo "WARNING: ${_dir} is already exists."
|
fi
|
mkdir -p "${_logdir}"
|
_data="${datadir}/${dset}/dumped_files"
|
key_file=${_data}/feats.scp
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
_opt=
|
if [ ! -z "${inference_config}" ]; then
|
_opt="--config ${inference_config}"
|
fi
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
echo "Inference log can be found at ${_logdir}/inference.*.log"
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
|
python -m funasr.bin.diar_inference_launch \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob} \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
|
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--diar_train_config "${exp_model_dir}"/config.yaml \
|
--diar_model_file "${exp_model_dir}"/${inference_model} \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode sond ${_opt}
|
done
|
fi
|
|
# Scoring for pretrained model, you may get a DER like 11.54 15.41
|
# 11.54: with oracle VAD, 15.41: with only SOND outputs, aka, system VAD.
|
if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
|
echo "stage 15: Scoring phase-2 models"
|
if [ ! -e dscore ]; then
|
git clone https://github.com/nryant/dscore.git
|
# add intervaltree to setup.py
|
fi
|
for dset in ${test_sets}; do
|
echo "stage 15: Scoring for ${dset}"
|
diar_exp=${expdir}/${model_dir}_phase2
|
_data="${datadir}/${dset}"
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
|
|
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
|
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
|
# echo ${cmd}
|
eval ${cmd}
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.ref_vad
|
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.sys_vad
|
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
|
done
|
fi
|
|
|
# Finetune Stage, phase 3, training on callhom1 training set
|
if [ ${stage} -le 16 ] && [ ${stop_stage} -ge 16 ]; then
|
echo "stage 16: training phase 3, finetuing on callhome1 real data"
|
world_size=$gpu_num # run on one machine
|
mkdir -p ${expdir}/${model_dir}_phase3
|
mkdir -p ${expdir}/${model_dir}_phase3/log
|
mkdir -p /tmp/${model_dir}_phase3
|
INIT_FILE=/tmp/${model_dir}_phase3/ddp_init
|
if [ -f $INIT_FILE ];then
|
rm -f $INIT_FILE
|
fi
|
init_opt=""
|
if [ ! -z "${init_param}" ]; then
|
init_opt="--init_param ${init_param}"
|
echo ${init_opt}
|
fi
|
|
freeze_opt=""
|
if [ ! -z "${freeze_param}" ]; then
|
freeze_opt="--freeze_param ${freeze_param}"
|
echo ${freeze_opt}
|
fi
|
|
phase3_config="$(dirname "${train_config}")/$(basename "${train_config}" .yaml)_phase3.yaml"
|
|
init_method=file://$(readlink -f $INIT_FILE)
|
echo "$0: init method is $init_method"
|
for ((i = 0; i < $gpu_num; ++i)); do
|
{
|
rank=$i
|
local_rank=$i
|
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
|
python -m funasr.bin.diar_train \
|
--gpu_id $gpu_id \
|
--use_preprocessor false \
|
--token_type char \
|
--token_list $token_list \
|
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--train_shape_file ${expdir}/${valid_set}_states/speech_shape \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
|
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
|
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
|
--init_param exp/${model_dir}_phase2/valid.forward_steps.ave_5best.pb \
|
--unused_parameters true \
|
${init_opt} \
|
${freeze_opt} \
|
--ignore_init_mismatch true \
|
--resume true \
|
--output_dir ${expdir}/${model_dir}_phase3 \
|
--config ${phase3_config} \
|
--ngpu $gpu_num \
|
--num_worker_count $count \
|
--multiprocessing_distributed true \
|
--dist_init_method $init_method \
|
--dist_world_size $world_size \
|
--dist_rank $rank \
|
--local_rank $local_rank 1> ${expdir}/${model_dir}_phase3/log/train.log.$i 2>&1
|
} &
|
done
|
echo "Training log can be found at ${expdir}/${model_dir}_phase3/log/train.log.*"
|
wait
|
fi
|
|
# evaluate for finetuned model
|
if [ ${stage} -le 17 ] && [ ${stop_stage} -ge 17 ]; then
|
echo "stage 17: evaluation for finetuned model ${inference_model}."
|
for dset in ${test_sets}; do
|
echo "Processing for $dset"
|
exp_model_dir=${expdir}/${model_dir}_phase3
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
if [ -d ${_dir} ]; then
|
echo "WARNING: ${_dir} is already exists."
|
fi
|
mkdir -p "${_logdir}"
|
_data="${datadir}/${dset}/dumped_files"
|
key_file=${_data}/feats.scp
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
_opt=
|
if [ ! -z "${inference_config}" ]; then
|
_opt="--config ${inference_config}"
|
fi
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
echo "Inference log can be found at ${_logdir}/inference.*.log"
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
|
python -m funasr.bin.diar_inference_launch \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob} \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
|
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--diar_train_config "${exp_model_dir}"/config.yaml \
|
--diar_model_file "${exp_model_dir}"/${inference_model} \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode sond ${_opt}
|
done
|
fi
|
|
# average 3 4 5 6 7 epoch
|
# Scoring for pretrained model, you may get a DER like
|
# 7.21 8.05 on callhome1
|
# 8.31 9.32 on callhome2
|
if [ ${stage} -le 18 ] && [ ${stop_stage} -ge 18 ]; then
|
echo "stage 18: Scoring finetuned models"
|
if [ ! -e dscore ]; then
|
git clone https://github.com/nryant/dscore.git
|
# add intervaltree to setup.py
|
fi
|
for dset in ${test_sets}; do
|
echo "stage 18: Scoring for ${dset}"
|
diar_exp=${expdir}/${model_dir}_phase3
|
_data="${datadir}/${dset}"
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
|
_logdir="${_dir}/logdir"
|
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
|
|
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
|
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
|
echo ${cmd}
|
eval ${cmd}
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.ref_vad
|
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${_dir}/sys.rttm.sys_vad
|
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
|
done
|
fi
|
|
|
if [ ${stage} -le 19 ] && [ ${stop_stage} -ge 19 ]; then
|
for dset in ${test_sets}; do
|
echo "stage 19: Evaluating phase-3 system on ${dset} set with medfilter_size=83 clustering=EEND-OLA"
|
sv_exp_dir=${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
|
diar_exp=${expdir}/${model_dir}_phase3
|
_data="${datadir}/${dset}/dumped_files"
|
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
|
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
|
|
for iter in `seq 0 ${told_max_iter}`; do
|
eval_dir=${_dir}/iter_${iter}
|
if [ $iter -eq 0 ]; then
|
prev_rttm=${expdir}/EEND-OLA/sys.rttm
|
else
|
prev_rttm=${_dir}/iter_$((${iter}-1))/sys.rttm.sys_vad
|
fi
|
echo "Use ${prev_rttm} as system outputs."
|
|
echo "Iteration ${iter}, step 1: extracting non-overlap segments"
|
cmd="python -Wignore script/extract_nonoverlap_segments.py ${datadir}/${dset}/wav.scp \
|
$prev_rttm ${eval_dir}/nonoverlap_segs/ --min_dur 0.1 --max_spk_num 16 --no_pbar --sr 8000"
|
# echo ${cmd}
|
eval ${cmd}
|
|
echo "Iteration ${iter}, step 2: make data directory"
|
mkdir -p ${eval_dir}/data
|
find `pwd`/${eval_dir}/nonoverlap_segs/ -iname "*.wav" | sort > ${eval_dir}/data/wav.flist
|
awk -F'[/.]' '{print $(NF-1),$0}' ${eval_dir}/data/wav.flist > ${eval_dir}/data/wav.scp
|
awk -F'[/.]' '{print $(NF-1),$(NF-2)}' ${eval_dir}/data/wav.flist > ${eval_dir}/data/utt2spk
|
cp $prev_rttm ${eval_dir}/data/sys.rttm
|
home_path=`pwd`
|
|
echo "Iteration ${iter}, step 3: calc x-vector for each utt"
|
key_file=${eval_dir}/data/wav.scp
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
_logdir=${eval_dir}/data/xvecs
|
mkdir -p ${_logdir}
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/sv_inference.JOB.log \
|
python -m funasr.bin.sv_inference_launch \
|
--njob ${njob} \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${key_file},speech,sound" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--sv_train_config ${sv_exp_dir}/sv.yaml \
|
--sv_model_file ${sv_exp_dir}/sv.pth \
|
--output_dir "${_logdir}"/output.JOB
|
cat ${_logdir}/output.*/xvector.scp | sort > ${eval_dir}/data/utt2xvec
|
|
echo "Iteration ${iter}, step 4: dump x-vector record"
|
awk '{print $1}' ${_data}/feats.scp > ${eval_dir}/data/idx
|
python script/dump_speaker_profiles.py --dir ${eval_dir}/data \
|
--out ${eval_dir}/global_n16 --n_spk 16 --no_pbar --emb_type global
|
spk_profile=${eval_dir}/global_n16_parts00_xvec.scp
|
|
echo "Iteration ${iter}, step 5: perform NN diarization"
|
_logdir=${eval_dir}/diar
|
mkdir -p ${_logdir}
|
key_file=${_data}/feats.scp
|
num_scp_file="$(<${key_file} wc -l)"
|
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
split_scps=
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
_opt=
|
if [ ! -z "${inference_config}" ]; then
|
_opt="--config ${inference_config}"
|
fi
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
echo "Inference log can be found at ${_logdir}/inference.*.log"
|
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
|
python -m funasr.bin.diar_inference_launch \
|
--batch_size 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob} \
|
--gpuid_list ${gpuid_list} \
|
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
|
--data_path_and_name_and_type "${spk_profile},profile,kaldi_ark" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--diar_train_config ${diar_exp}/config.yaml \
|
--diar_model_file ${diar_exp}/${inference_model} \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode sond ${_opt}
|
|
echo "Iteration ${iter}, step 6: calc diarization results"
|
cat ${_logdir}/output.*/labels.txt | sort > ${eval_dir}/labels.txt
|
|
cmd="python -Wignore script/convert_label_to_rttm.py ${eval_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${eval_dir}/sys.rttm \
|
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
|
# echo ${cmd}
|
eval ${cmd}
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${eval_dir}/sys.rttm.ref_vad
|
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
ref=${datadir}/${dset}/files_for_dump/ref.rttm
|
sys=${eval_dir}/sys.rttm.sys_vad
|
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
|
|
echo -e "${inference_model}/iter_${iter} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${eval_dir}/results.txt
|
done
|
|
echo "Done."
|
done
|
fi
|
