TOLD/SOND: modify data preprocessing

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2023-08-02 7664f364e6ed31b0be12f9107b4bd8b8be34dadd

 egs/callhome/diarization/sond/finetune.sh

@@ -30,6 +30,13 @@
  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
@@ -41,9 +48,6 @@
# number of jobs for data process
nj=16
sr=8000

# dataset related
callhome_root=path/to/NIST/LDC2001S97

# experiment configuration
lang=en
@@ -97,15 +101,18 @@

  # split ref.rttm
  for dset in callhome1 callhome2; do
    rm -rf data/${dset}/ref.rttm
    for name in `awk '{print $1}' data/${dset}/wav.scp`; do
      grep ${name} data/callhome/fullref.rttm >> data/${dset}/ref.rttm;
    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 $2}' data/${dset}/ref.rttm | sort | uniq > data/${dset}/uttid
    mv data/${dset}/wav.scp data/${dset}/wav.scp.bak
    awk '{if (NR==FNR){a[$1]=1}else{if (a[$1]==1){print $0}}}' data/${dset}/uttid data/${dset}/wav.scp.bak > data/${dset}/wav.scp
    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

@@ -157,16 +164,9 @@

  ln -s ${kaldi_root}/egs/callhome_diarization/v2/steps ./
  for dset in callhome1 callhome2; do
    mv ${datadir}/${dset}/segments ${datadir}/${dset}/segs
    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
    utils/fix_data_dir.sh ${datadir}/${dset}
  done

  for dset in callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
    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
    utils/fix_data_dir.sh ${datadir}/${dset}
  done
  rm -f steps

@@ -174,14 +174,16 @@

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
  echo "Stage 4: Extract speaker embeddings."
  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}/

  sv_exp_dir=exp/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
  sed "s/input_size: null/input_size: 80/g" ${sv_exp_dir}/sv.yaml > ${sv_exp_dir}/sv_fbank.yaml

  if [ ! -e ${sv_exp_dir} ]; 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 callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
    key_file=${datadir}/${dset}/feats.scp
    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
@@ -199,9 +201,9 @@
        --njob ${njob} \
        --ngpu "${_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_fbank.yaml \
        --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