init repo

嘉渊

2023-08-05 47343b5c2f4e1256f60f46d8da0aa2e5de39b6c7

 egs/callhome/eend_ola/run.sh

@@ -3,19 +3,23 @@
. ./path.sh || exit 1;

# machines configuration
CUDA_VISIBLE_DEVICES="7"
CUDA_VISIBLE_DEVICES="0"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
count=1

# general configuration
simu_feats_dir="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data/data"
simu_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data_chunk2000/data"
callhome_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/callhome_chunk2000/data"
dump_cmd=utils/run.pl
nj=64

# feature configuration
data_dir="./data"
simu_feats_dir=$data_dir/ark_data/dump/simu_data/data
simu_feats_dir_chunk2000=$data_dir/ark_data/dump/simu_data_chunk2000/data
callhome_feats_dir_chunk2000=$data_dir/ark_data/dump/callhome_chunk2000/data
simu_train_dataset=train
simu_valid_dataset=dev
callhome_train_dataset=callhome1_allspk
callhome_valid_dataset=callhome2_allspk
callhome2_wav_scp_file=wav.scp
callhome_train_dataset=callhome1_spkall
callhome_valid_dataset=callhome2_spkall

# model average
simu_average_2spkr_start=91
@@ -27,11 +31,11 @@

exp_dir="."
input_size=345
stage=-1
stop_stage=-1
stage=1
stop_stage=5

# exp tag
tag="exp_fix"
tag="exp1"

. local/parse_options.sh || exit 1;

@@ -52,7 +56,7 @@

# simulate mixture data for training and inference
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
    echo "stage 0: Simulate mixture data for training and inference"
    echo "stage -1: Simulate mixture data for training and inference"
    echo "The detail can be found in https://github.com/hitachi-speech/EEND"
    echo "Before running this step, you should download and compile kaldi and set KALDI_ROOT in this script and path.sh"
    echo "This stage may take a long time, please waiting..."
@@ -62,13 +66,72 @@
    local/run_prepare_shared_eda.sh
fi

## Prepare data for training and inference
#if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
#    echo "stage 0: Prepare data for training and inference"
#    echo "The detail can be found in https://github.com/hitachi-speech/EEND"
#    . ./local/
#fi
#
# Prepare data for training and inference
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    echo "stage 0: Prepare data for training and inference"
    simu_opts_num_speaker_array=(1 2 3 4)
    simu_opts_sil_scale_array=(2 2 5 9)
    simu_opts_num_train=100000

    # for simulated data of chunk500 and chunk2000
    for dset in swb_sre_cv swb_sre_tr; do
        if [ "$dset" == "swb_sre_tr" ]; then
            n_mixtures=${simu_opts_num_train}
            dataset=train
        else
            n_mixtures=500
            dataset=dev
        fi
        simu_data_dir=${dset}_ns"$(IFS="n"; echo "${simu_opts_num_speaker_array[*]}")"_beta"$(IFS="n"; echo "${simu_opts_sil_scale_array[*]}")"_${n_mixtures}
        mkdir -p ${data_dir}/simu/data/${simu_data_dir}/.work
        split_scps=
        for n in $(seq $nj); do
            split_scps="$split_scps ${data_dir}/simu/data/${simu_data_dir}/.work/wav.scp.$n"
        done
        utils/split_scp.pl "${data_dir}/simu/data/${simu_data_dir}/wav.scp" $split_scps || exit 1
        python local/split.py ${data_dir}/simu/data/${simu_data_dir}
        # for chunk_size=500
        output_dir=${data_dir}/ark_data/dump/simu_data/$dataset
        mkdir -p $output_dir/.logs
        $dump_cmd --max-jobs-run $nj JOB=1:$nj $output_dir/.logs/dump.JOB.log \
        python local/dump_feature.py \
              --data_dir ${data_dir}/simu/data/${simu_data_dir}/.work \
              --output_dir $output_dir \
              --index JOB
        mkdir -p ${data_dir}/ark_data/dump/simu_data/data/$dataset
        cat ${data_dir}/ark_data/dump/simu_data/$dataset/feature.scp.* > ${data_dir}/ark_data/dump/simu_data/data/$dataset/feature.scp
        cat ${data_dir}/ark_data/dump/simu_data/$dataset/label.scp.* > ${data_dir}/ark_data/dump/simu_data/data/$dataset/label.scp
        paste -d" " ${data_dir}/ark_data/dump/simu_data/data/$dataset/feature.scp <(cut -f2 -d" " ${data_dir}/ark_data/dump/simu_data/data/$dataset/label.scp) > ${data_dir}/ark_data/dump/simu_data/data/$dataset/feats.scp
        grep "ns2" ${data_dir}/ark_data/dump/simu_data/data/$dataset/feats.scp > ${data_dir}/ark_data/dump/simu_data/data/$dataset/feats_2spkr.scp
        # for chunk_size=2000
        output_dir=${data_dir}/ark_data/dump/simu_data_chunk2000/$dataset
        mkdir -p $output_dir/.logs
        $dump_cmd --max-jobs-run $nj JOB=1:$nj $output_dir/.logs/dump.JOB.log \
        python local/dump_feature.py \
              --data_dir ${data_dir}/simu/data/${simu_data_dir}/.work \
              --output_dir $output_dir \
              --index JOB \
              --num_frames 2000
        mkdir -p ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset
        cat ${data_dir}/ark_data/dump/simu_data_chunk2000/$dataset/feature.scp.* > ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/feature.scp
        cat ${data_dir}/ark_data/dump/simu_data_chunk2000/$dataset/label.scp.* > ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/label.scp
        paste -d" " ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/feature.scp <(cut -f2 -d" " ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/label.scp) > ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/feats.scp
    done

    # for callhome data
    for dset in callhome1_spkall callhome2_spkall; do
        find  $data_dir/eval/$dset  -maxdepth 1 -type f -exec cp {} {}.1 \;
        output_dir=${data_dir}/ark_data/dump/callhome_chunk2000/$dset
        mkdir -p $output_dir
        python local/dump_feature.py \
              --data_dir $data_dir/eval/$dset \
              --output_dir $output_dir \
              --index 1 \
              --num_frames 2000
        mkdir -p ${data_dir}/ark_data/dump/callhome_chunk2000/data/$dset
        paste -d" " ${data_dir}/ark_data/dump/callhome_chunk2000/$dset/feature.scp.1 <(cut -f2 -d" " ${data_dir}/ark_data/dump/callhome_chunk2000/$dset/label.scp.1) > ${data_dir}/ark_data/dump/callhome_chunk2000/data/$dset/feats.scp
    done
fi

# Training on simulated two-speaker data
world_size=$gpu_num
@@ -245,13 +308,17 @@
    python local/model_averaging.py ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb $models
fi

## inference
#if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
#    echo "Inference"
#    mkdir -p ${exp_dir}/exp/${callhome_model_dir}/inference/log
#    CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python local/infer.py \
#        --config_file ${exp_dir}/exp/${callhome_model_dir}/config.yaml \
#        --model_file ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb \
#        --output_rttm_file ${exp_dir}/exp/${callhome_model_dir}/inference/rttm \
#        --wav_scp_file ${callhome_feats_dir_chunk2000}/${callhome_valid_dataset}/${callhome2_wav_scp_file} 1> ${exp_dir}/exp/${callhome_model_dir}/inference/log/infer.log 2>&1
#fi
# inference and compute DER
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    echo "Inference"
    mkdir -p ${exp_dir}/exp/${callhome_model_dir}/inference/log
    CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python local/infer.py \
        --config_file ${exp_dir}/exp/${callhome_model_dir}/config.yaml \
        --model_file ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb \
        --output_rttm_file ${exp_dir}/exp/${callhome_model_dir}/inference/rttm \
        --wav_scp_file $data_dir/eval/callhome2_spkall/wav.scp \
        1> ${exp_dir}/exp/${callhome_model_dir}/inference/log/infer.log 2>&1
    md-eval.pl -c 0.25 \
          -r ${data_dir}/eval/${callhome_valid_dataset}/rttm \
          -s ${exp_dir}/exp/${callhome_model_dir}/inference/rttm > ${exp_dir}/exp/${callhome_model_dir}/inference/result_med11_collar0.25 2>/dev/null || exit
fi