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FunASR/egs/callhome/TOLD/soap/finetune.sh
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2023-08-07 16:20:11 +08:00

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#!/usr/bin/env bash
. ./path.sh || exit 1;
# This recipe aims at reimplement the results of SOND on Callhome corpus which is represented in
# [1] TOLD: A Novel Two-stage Overlap-aware Framework for Speaker Diarization, ICASSP 2023
# You can also use it on other dataset such AliMeeting to reproduce the results in
# [2] Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis, EMNLP 2022
# We recommend you run this script stage by stage.
# This recipe includes:
# 1. downloading a pretrained model on the simulated data from switchboard and NIST,
# 2. finetuning the pretrained model on Callhome1.
# Finally, you will get a slightly better DER result 9.95% on Callhome2 than that in the paper 10.14%.
# environment configuration
kaldi_root=
if [ -z "${kaldi_root}" ]; then
echo "We need kaldi to prepare dataset, extract fbank features, please install kaldi first and set kaldi_root."
echo "Kaldi installation guide can be found at https://kaldi-asr.org/"
exit;
fi
if [ ! -e local ]; then
ln -s ${kaldi_root}/egs/callhome_diarization/v2/local ./local
fi
if [ ! -e utils ]; then
ln -s ${kaldi_root}/egs/callhome_diarization/v2/utils ./utils
fi
# callhome data root like path/to/NIST/LDC2001S97
callhome_root=
if [ -z "${kaldi_root}" ]; then
echo "We need callhome corpus to prepare data."
exit;
fi
# machines configuration
gpu_devices="0,1,2,3" # for V100-16G, need 4 gpus.
gpu_num=4
count=1
# general configuration
stage=0
stop_stage=10
# number of jobs for data process
nj=16
sr=8000
# experiment configuration
lang=en
feats_type=fbank
datadir=data
dumpdir=dump
expdir=exp
train_cmd=utils/run.pl
# training related
tag=""
train_set=callhome1
valid_set=callhome1
train_config=conf/EAND_ResNet34_SAN_L4N512_None_FFN_FSMN_L6N512_bce_dia_loss_01_phase3.yaml
token_list=${datadir}/token_list/powerset_label_n16k4.txt
init_param=
freeze_param=
# inference related
inference_model=valid.der.ave_5best.pb
inference_config=conf/basic_inference.yaml
inference_tag=""
test_sets="callhome2"
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
# number of jobs for inference
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=4
infer_cmd=utils/run.pl
told_max_iter=4
. utils/parse_options.sh || exit 1;
model_dir="$(basename "${train_config}" .yaml)_${feats_type}_${lang}${tag}"
# you can set gpu num for decoding here
gpuid_list=$gpu_devices # set gpus for decoding, the same as training stage by default
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
if ${gpu_inference}; then
inference_nj=$[${ngpu}*${njob}]
_ngpu=1
else
inference_nj=$njob
_ngpu=0
fi
# Prepare datasets
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "Stage 0: Prepare callhome data."
local/make_callhome.sh ${callhome_root} ${datadir}/
# split ref.rttm
for dset in callhome1 callhome2; do
rm -rf ${datadir}/${dset}/ref.rttm
for name in `awk '{print $1}' ${datadir}/${dset}/wav.scp`; do
grep ${name} ${datadir}/callhome/fullref.rttm >> ${datadir}/${dset}/ref.rttm;
done
# filter out records which don't have rttm labels.
awk '{print $2}' ${datadir}/${dset}/ref.rttm | sort | uniq > ${datadir}/${dset}/uttid
mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/wav.scp.bak
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
mkdir ${datadir}/${dset}/raw
mv ${datadir}/${dset}/{reco2num_spk,segments,spk2utt,utt2spk,uttid,wav.scp.bak} ${datadir}/${dset}/raw/
awk '{print $1,$1}' ${datadir}/${dset}/wav.scp > ${datadir}/${dset}/utt2spk
done
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Dump sph file to wav"
export PATH=${kaldi_root}/tools/sph2pipe/:${PATH}
if [ ! -f ${kaldi_root}/tools/sph2pipe/sph2pipe ]; then
echo "Can not find sph2pipe in ${kaldi_root}/tools/sph2pipe/,"
echo "please install sph2pipe and put it in the right place."
exit;
fi
for dset in callhome1 callhome2; do
echo "Stage 1: start to dump ${dset}."
mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/sph.scp
mkdir -p ${dumpdir}/${dset}/wavs
python -Wignore script/dump_pipe_wav.py ${datadir}/${dset}/sph.scp ${dumpdir}/${dset}/wavs \
--sr ${sr} --nj ${nj} --no_pbar
find `pwd`/${dumpdir}/${dset}/wavs -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/wav.scp
done
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Extract non-overlap segments from callhome dataset"
for dset in callhome1 callhome2; do
echo "Stage 2: Extracting non-overlap segments for "${dset}
mkdir -p ${dumpdir}/${dset}/nonoverlap_0s
python -Wignore script/extract_nonoverlap_segments.py \
${datadir}/${dset}/wav.scp ${datadir}/${dset}/ref.rttm ${dumpdir}/${dset}/nonoverlap_0s \
--min_dur 0.1 --max_spk_num 8 --sr ${sr} --no_pbar --nj ${nj}
mkdir -p ${datadir}/${dset}/nonoverlap_0s
find ${dumpdir}/${dset}/nonoverlap_0s/ -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/nonoverlap_0s/wav.scp
awk -F'[/.]' '{print $(NF-1),$(NF-2)}' ${datadir}/${dset}/nonoverlap_0s/wav.scp > ${datadir}/${dset}/nonoverlap_0s/utt2spk
echo "Done."
done
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "Stage 3: Generate fbank features"
home_path=$(pwd)
cd ${kaldi_root}/egs/callhome_diarization/v2 || exit
export train_cmd="run.pl"
export cmd="run.pl"
. ./path.sh
cd $home_path || exit
ln -s ${kaldi_root}/egs/callhome_diarization/v2/steps ./
for dset in callhome1 callhome2; do
utils/fix_data_dir.sh ${datadir}/${dset}
steps/make_fbank.sh --write-utt2num-frames true --fbank-config conf/fbank.conf --nj ${nj} --cmd "$train_cmd" \
${datadir}/${dset} ${expdir}/make_fbank/${dset} ${dumpdir}/${dset}/fbank
done
rm -f steps
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "Stage 4: Extract speaker embeddings."
sv_exp_dir=exp/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
if [ ! -e ${sv_exp_dir} ]; then
echo "start to download sv models"
git lfs install
git clone https://www.modelscope.cn/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch.git
mv speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ${expdir}/
echo "Done."
fi
for dset in callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
echo "Start to extract speaker embeddings for ${dset}"
key_file=${datadir}/${dset}/wav.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
_logdir=${dumpdir}/${dset}/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 \
--batch_size 1 \
--njob ${njob} \
--ngpu "${_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 > ${datadir}/${dset}/utt2xvec
python script/calc_num_frames.py ${key_file} ${datadir}/${dset}/utt2num_frames
echo "Done."
done
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "Stage 5: Generate label files."
for dset in callhome1 callhome2; do
echo "Stage 5: Generate labels for ${dset}."
python -Wignore script/calc_real_meeting_frame_labels.py \
${datadir}/${dset} ${dumpdir}/${dset}/labels \
--n_spk 8 --frame_shift 0.01 --nj 16 --sr 8000
find `pwd`/${dumpdir}/${dset}/labels/ -iname "*.lbl.mat" | awk -F'[/.]' '{print $(NF-2),$0}' | sort > ${datadir}/${dset}/labels.scp
done
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Stage 6: Make training and evaluation files."
# dump callhome1 data in training mode.
data_dir=${datadir}/callhome1/files_for_dump
mkdir ${data_dir}
# filter out zero duration segments
LC_ALL=C awk '{if ($5 > 0){print $0}}' ${datadir}/callhome1/ref.rttm > ${data_dir}/ref.rttm
cp ${datadir}/callhome1/{feats.scp,labels.scp} ${data_dir}/
cp ${datadir}/callhome1/nonoverlap_0s/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
echo "Stage 6: start to dump for callhome1."
python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
--out ${dumpdir}/callhome1/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode train \
--chunk_size 1600 --chunk_shift 400 --add_mid_to_speaker true
mkdir -p ${datadir}/callhome1/dumped_files
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 6: 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
# Finetune model on callhome1, this will take about 1.5 hours.
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
echo "Stage 7: Finetune pretrained model on callhome1."
if [ ! -e ${expdir}/speech_diarization_sond-en-us-swbd_sre-8k-n16k4-pytorch ]; then
echo "start to download pretrained models"
git lfs install
git clone https://www.modelscope.cn/damo/speech_diarization_sond-en-us-swbd_sre-8k-n16k4-pytorch.git
mv speech_diarization_sond-en-us-swbd_sre-8k-n16k4-pytorch ${expdir}/
echo "Done."
fi
world_size=$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 ${datadir}/${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 ${expdir}/speech_diarization_sond-en-us-swbd_sre-8k-n16k4-pytorch/sond.pth \
--unused_parameters true \
${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 finetuned model
if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
echo "stage 8: evaluation for finetuned model ${inference_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 finetuned model, you may get a DER like:
# oracle_vad | system_vad
# 7.32 | 8.14
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
echo "stage 9: Scoring finetuned models"
if [ ! -e dscore ]; then
git clone https://github.com/nryant/dscore.git
pip install intervaltree
# add intervaltree to setup.py
fi
for dset in ${test_sets}; do
echo "stage 9: 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
# In this stage, we need the raw waveform files of Callhome corpus.
# Due to the data license, we can't provide them, please get them additionally.
# And convert the sph files to wav files (use scripts/dump_pipe_wav.py).
# Then find the wav files to construct wav.scp and put it at data/callhome2/wav.scp.
# After iteratively perform SOAP, you will get DER results like:
# iters : oracle_vad | system_vad
# iter_0: 9.58 | 10.46
# iter_1: 9.22 | 10.15
# iter_2: 9.21 | 10.14
# iter_3: 9.30 | 10.24
# iter_4: 9.29 | 10.23
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
if [ ! -e ${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ]; then
git lfs install
git clone https://www.modelscope.cn/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch.git
mv speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ${expdir}/
fi
for dset in ${test_sets}; do
echo "stage 10: Evaluating finetuned 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}
_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
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