From 4fdcba65bb35c0897be06840779b1690254c7383 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期二, 01 八月 2023 20:19:58 +0800
Subject: [PATCH] TOLD/SOND: fixbug extract null segments
---
egs/callhome/diarization/sond/finetune.sh | 347 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
egs/callhome/diarization/sond/run.sh | 2
egs/callhome/diarization/sond/script/extract_nonoverlap_segments.py | 2
3 files changed, 349 insertions(+), 2 deletions(-)
diff --git a/egs/callhome/diarization/sond/finetune.sh b/egs/callhome/diarization/sond/finetune.sh
new file mode 100644
index 0000000..e82dcae
--- /dev/null
+++ b/egs/callhome/diarization/sond/finetune.sh
@@ -0,0 +1,347 @@
+#!/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.
+
+# environment configuration
+if [ ! -e utils ]; then
+ ln -s ../../../aishell/transformer/utils ./utils
+fi
+
+# machines configuration
+gpu_devices="0,1,2,3"
+gpu_num=4
+count=1
+
+# general configuration
+stage=1
+stop_stage=1
+# number of jobs for data process
+nj=16
+sr=8000
+
+# dataset related
+data_root=
+
+# 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=${datadir}/token_list/powerset_label_n16k4.txt
+init_param=
+freeze_param=
+
+# inference related
+inference_model=valid.der.ave_5best.pth
+inference_config=conf/basic_inference.yaml
+inference_tag=""
+test_sets="callhome2"
+gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
+# number of jobs for inference
+# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
+njob=4
+infer_cmd=utils/run.pl
+told_max_iter=4
+
+. utils/parse_options.sh || exit 1;
+
+model_dir="$(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
+
+# Download required resources
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+ echo "Stage 0: Download required resources."
+ wget told_finetune_resources.zip
+fi
+
+# Finetune model on callhome1
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+ echo "Stage 1: Finetune pretrained model on callhome1."
+ world_size=$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])
+ diar_train.py \
+ --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/pretrained_models/phase2.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 2 ] && [ ${stop_stage} -ge 2 ]; then
+ echo "stage 2: 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
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ echo "stage 3: 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 3: 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
+
+
+# You will get a DER like:
+# iter0: 9.68 10.51
+# iter1:
+# iter2:
+# iter3:
+# iter4:
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+ for dset in ${test_sets}; do
+ echo "stage 4: 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}_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 ${eval_dir}/results.txt
+ done
+
+ echo "Done."
+ done
+fi
diff --git a/egs/callhome/diarization/sond/run.sh b/egs/callhome/diarization/sond/run.sh
index 0497558..3758f0c 100644
--- a/egs/callhome/diarization/sond/run.sh
+++ b/egs/callhome/diarization/sond/run.sh
@@ -876,7 +876,7 @@
--batch_size 1 \
--ngpu "${_ngpu}" \
--gpuid_list ${gpuid_list} \
- --data_path_and_name_and_type "${key_file},speech,kaldi_ark" \
+ --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 \
diff --git a/egs/callhome/diarization/sond/script/extract_nonoverlap_segments.py b/egs/callhome/diarization/sond/script/extract_nonoverlap_segments.py
index 8617c59..a4e43a2 100644
--- a/egs/callhome/diarization/sond/script/extract_nonoverlap_segments.py
+++ b/egs/callhome/diarization/sond/script/extract_nonoverlap_segments.py
@@ -93,7 +93,7 @@
extracted_spk = []
count = 1
for st, ed, spk in tqdm(turns, total=len(turns), ascii=True, disable=args.no_pbar):
- if (ed - st) >= args.min_dur * args.sr:
+ if (ed - st) >= args.min_dur * args.sr and wav[st: ed] >= args.min_dur * args.sr:
seg = wav[st: ed]
save_path = os.path.join(args.out_dir, mid, spk, "{}_U{:04d}.wav".format(spk, count))
if not os.path.exists(os.path.dirname(save_path)):
--
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