#!/usr/bin/env bash
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set -e
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set -o pipefail
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. path.sh || exit 1
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train_cmd=utils/run.pl
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# data path
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data_source_dir=$DATA_SOURCE
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textgrid_dir=$data_source_dir/textgrid_dir/
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wav_dir=$data_source_dir/audio_dir/
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# work path
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work_dir=./data/${DATA_NAME}_sc/
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sad_dir=$work_dir/sad_part/
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sad_work_dir=$sad_dir/exp/
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sad_result_dir=$sad_dir/sad
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dia_dir=$work_dir/dia_part/
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dia_vad_dir=$dia_dir/vad/
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dia_rttm_dir=$dia_dir/rttm/
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dia_emb_dir=$dia_dir/embedding/
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dia_rtt_label_dir=$dia_dir/label_rttm/
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dia_result_dir=$dia_dir/result_DER/
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sond_work_dir=./data/${DATA_NAME}_sond/
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asr_work_dir=./data/${DATA_NAME}_wpegss/org/
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mkdir -p $work_dir || exit 1;
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mkdir -p $sad_dir || exit 1;
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mkdir -p $sad_work_dir || exit 1;
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mkdir -p $sad_result_dir || exit 1;
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mkdir -p $dia_dir || exit 1;
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mkdir -p $dia_vad_dir || exit 1;
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mkdir -p $dia_rttm_dir || exit 1;
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mkdir -p $dia_emb_dir || exit 1;
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mkdir -p $dia_rtt_label_dir || exit 1;
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mkdir -p $dia_result_dir || exit 1;
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mkdir -p $sond_work_dir || exit 1;
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mkdir -p $asr_work_dir || exit 1;
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stage=0
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stop_stage=9
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nj=4
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sm_size=83
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if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# Check the installtion of kaldi
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if [ -L ./steps ]; then
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unlink ./steps
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else
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ln -s $KALDI_ROOT/egs/wsj/s5/steps || { echo "You must install kaldi first, and set the KALDI_ROOT in path.sh" && exit 1; }
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fi
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if [ -L ./utils ]; then
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unlink ./utils
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else
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ln -s $KALDI_ROOT/egs/wsj/s5/utils || { echo "You must install kaldi first, and set the KALDI_ROOT in path.sh" && exit 1; }
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fi
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fi
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if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# Prepare the AliMeeting data
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echo "Prepare Alimeeting data"
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find $wav_dir -name "*\.wav" > $work_dir/wavlist
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sort $work_dir/wavlist > $work_dir/tmp
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cp $work_dir/tmp $work_dir/wavlist
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awk -F '/' '{print $NF}' $work_dir/wavlist | awk -F '.' '{print $1}' > $work_dir/uttid
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paste -d " " $work_dir/uttid $work_dir/wavlist > $work_dir/wav.scp
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paste -d " " $work_dir/uttid $work_dir/uttid > $work_dir/utt2spk
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cp $work_dir/utt2spk $work_dir/spk2utt
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cp $work_dir/uttid $work_dir/text
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sad_feat=$sad_dir/feat/mfcc
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cp $work_dir/wav.scp $sad_dir
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cp $work_dir/utt2spk $sad_dir
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cp $work_dir/spk2utt $sad_dir
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cp $work_dir/text $sad_dir
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utils/fix_data_dir.sh $sad_dir
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## first we extract the feature for sad model
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steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" \
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--mfcc-config conf/mfcc_hires.conf \
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$sad_dir $sad_dir/make_mfcc $sad_feat
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fi
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if [ $stage -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# Do Speech Activity Detectation
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echo "Do SAD"
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./utils/split_data.sh $sad_dir $nj
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## do the segmentations
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local/segmentation/detect_speech_activity.sh --nj $nj --stage 0 \
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--cmd "$train_cmd" $sad_dir exp/segmentation_1a/tdnn_stats_sad_1a/ \
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$sad_dir/feat/mfcc $sad_work_dir $sad_result_dir
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fi
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if [ $stage -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "Do Speaker Embedding Extractor"
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cp $work_dir/wav.scp $dia_dir
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python local/segment_to_lab.py --input_segments $sad_dir/sad_seg/segments \
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--label_path $dia_vad_dir \
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--output_label_scp_file $dia_dir/label.scp ||exit 1;
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./utils/split_data.sh $work_dir $nj
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${train_cmd} JOB=1:${nj} $dia_dir/exp/extract_embedding.JOB.log \
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python VBx/predict.py --in-file-list $work_dir/split${nj}/JOB/text \
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--in-lab-dir $dia_dir/vad \
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--in-wav-dir $wav_dir \
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--out-ark-fn $dia_emb_dir/embedding_out.JOB.ark \
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--out-seg-fn $dia_emb_dir/embedding_out.JOB.seg \
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--weights VBx/models/ResNet101_16kHz/nnet/final.onnx \
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--backend onnx
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echo "success"
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fi
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if [ $stage -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# The Speaker Embedding Cluster
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echo "Do the Speaker Embedding Cluster"
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# The meeting data is long so that the cluster is a little bit slow
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${train_cmd} JOB=1:${nj} $dia_dir/exp/cluster.JOB.log \
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python VBx/vbhmm.py --init AHC+VB \
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--out-rttm-dir $dia_rttm_dir \
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--xvec-ark-file $dia_emb_dir/embedding_out.JOB.ark \
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--segments-file $dia_emb_dir/embedding_out.JOB.seg \
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--xvec-transform VBx/models/ResNet101_16kHz/transform.h5 \
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--plda-file VBx/models/ResNet101_16kHz/plda \
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--threshold 0.14 \
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--lda-dim 128 \
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--Fa 0.3 \
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--Fb 17 \
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--loopP 0.99
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fi
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if [ $stage -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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echo "Process textgrid to obtain rttm label"
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find -L $textgrid_dir -iname "*.TextGrid" > $work_dir/textgrid.flist
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sort $work_dir/textgrid.flist > $work_dir/tmp
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cp $work_dir/tmp $work_dir/textgrid.flist
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paste $work_dir/uttid $work_dir/textgrid.flist > $work_dir/uttid_textgrid.flist
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while read text_file
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do
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text_grid=`echo $text_file | awk '{print $1}'`
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text_grid_path=`echo $text_file | awk '{print $2}'`
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python local/make_textgrid_rttm.py --input_textgrid_file $text_grid_path \
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--uttid $text_grid \
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--output_rttm_file $dia_rtt_label_dir/${text_grid}.rttm
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done < $work_dir/uttid_textgrid.flist
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if [ -f "$dia_rtt_label_dir/all.rttm" ]; then
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rm -f $dia_rtt_label_dir/all.rttm
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fi
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cat $dia_rtt_label_dir/*.rttm > $dia_rtt_label_dir/all.rttm
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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echo "Get VBx DER result"
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find $dia_rtt_label_dir -name "*.rttm" > $dia_rtt_label_dir/ref.scp
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find $dia_rttm_dir -name "*.rttm" > $dia_rttm_dir/sys.scp
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if [ -f "$dia_rttm_dir/all.rttm" ]; then
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rm -f $dia_rttm_dir/all.rttm
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fi
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cat $dia_rttm_dir/*.rttm > $dia_rttm_dir/all.rttm
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collar_set="0 0.25"
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python local/meeting_speaker_number_process.py --path=$work_dir \
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--label_path=$dia_rtt_label_dir --predict_path=$dia_rttm_dir
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speaker_number="2 3 4"
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for weight_collar in $collar_set;
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do
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# all meeting
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python dscore/score.py --collar $weight_collar \
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-R $dia_rtt_label_dir/ref.scp -S $dia_rttm_dir/sys.scp > $dia_result_dir/speaker_all_DER_overlaps_${weight_collar}.log
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# 2,3,4 speaker meeting
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for speaker_count in $speaker_number;
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do
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python dscore/score.py --collar $weight_collar \
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-R $dia_rtt_label_dir/speaker${speaker_count}_id -S $dia_rttm_dir/speaker${speaker_count}_id > $dia_result_dir/speaker_${speaker_count}_DER_overlaps_${weight_collar}.log
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done
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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 "Downloading Pre-trained model..."
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mkdir ./SOND
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cd ./SOND
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git clone https://www.modelscope.cn/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch.git
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git clone https://www.modelscope.cn/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch.git
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ln -s speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth ./sv.pb
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cp speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.yaml ./sv.yaml
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ln -s speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.pth ./sond.pb
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cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond_fbank.yaml ./sond_fbank.yaml
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cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.yaml ./sond.yaml
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cd ..
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fi
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if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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echo "Prepare data for sond"
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cp $work_dir/wav.scp $sond_work_dir
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# convert rttm to segments
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python local/rttm2segments.py $dia_rttm_dir/all.rttm $sond_work_dir 0
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# remove the overlapped part
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python local/remove_overlap.py $sond_work_dir/segments $sond_work_dir/utt2spk \
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$sond_work_dir/segments_nooverlap $sond_work_dir/utt2spk_nooverlap 0.3
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# extract speaker profile from the filtered segments file
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python local/extract_profile_from_segments.py $sond_work_dir
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# segment data to 16s
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python local/resegment_data.py \
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$data_source_dir/segments \
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$data_source_dir/wav.scp \
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$sond_work_dir
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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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echo "Diarization with SOND"
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python local/infer_sond.py SOND/sond.yaml SOND/sond.pb $sond_work_dir $sond_work_dir/dia_outputs
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python local/convert_label_to_rttm.py \
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$sond_work_dir/dia_outputs/labels.txt \
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$sond_work_dir/map.scp \
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$sond_work_dir/dia_outputs/prediction_sm_${sm_size}.rttm \
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--ignore_len 10 --no_pbar --smooth_size ${sm_size} \
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--vote_prob 0.5 --n_spk 16
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python dscore/score.py \
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-r $dia_rtt_label_dir/all.rttm \
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-s $sond_work_dir/dia_outputs/prediction_sm_${sm_size}.rttm \
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--collar 0.25 &> $sond_work_dir/dia_outputs/dia_result
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# convert rttm to segments
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python local/rttm2segments.py $sond_work_dir/dia_outputs/prediction_sm_${sm_size}.rttm $asr_work_dir 1
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fi
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