update

嘉渊

2023-07-21 d273f7e12693e5b366cbf2ff7d01dde0264b01d9

 egs/callhome/eend_ola/run.sh

@@ -8,6 +8,11 @@
count=1

# general configuration
dump_cmd=utils/run.pl
nj=64

# feature configuration
data_dir="./data"
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"
@@ -31,7 +36,7 @@
stop_stage=-1

# exp tag
tag="exp_fix"
tag="exp1"

. local/parse_options.sh || exit 1;

@@ -52,23 +57,40 @@

# 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..."
    KALDI_ROOT=
    ln -s $KALDI_ROOT/egs/wsj/s5/steps steps
    ln -s $KALDI_ROOT/egs/wsj/s5/utils utils
    . local/run_prepare_shared_eda.sh
    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
    for dset in swb_sre_tr swb_sre_cv; do
        if [ "$dset" == "swb_sre_tr" ]; then
            n_mixtures=${simu_opts_num_train}
        else
            n_mixtures=500
        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.$n.scp"
        done
        utils/split_scp.pl "${data_dir}/simu/data/${simu_data_dir}/wav.scp" $split_scps || exit 1
        python local/split.py ${data_dir}/simu/data/${simu_data_dir}
    done
fi

# Training on simulated two-speaker data
world_size=$gpu_num
@@ -245,13 +267,17 @@
    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 ${callhome_feats_dir_chunk2000}/${callhome_valid_dataset}/${callhome2_wav_scp_file} \
        1> ${exp_dir}/exp/${callhome_model_dir}/inference/log/infer.log 2>&1
    md-eval.pl -c 0.25 \
          -r ${callhome_feats_dir_chunk2000}/${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