Merge pull request #807 from alibaba-damo-academy/dev_wjm

jmwang66

2023-08-07 cf8e000a84e888495dcf30c4dbfecea1ee7ab4e2

 egs/callhome/eend_ola/run.sh

@@ -99,10 +99,9 @@
              --output_dir $output_dir \
              --index JOB
        mkdir -p ${data_dir}/ark_data/dump/simu_data/data/$dataset
        python local/gen_feats_scp.py \
              --root_path ${data_dir}/ark_data/dump/simu_data/$dataset \
              --out_path ${data_dir}/ark_data/dump/simu_data/data/$dataset \
              --split_num $nj
        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
@@ -114,28 +113,23 @@
              --index JOB \
              --num_frames 2000
        mkdir -p ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset
        python local/gen_feats_scp.py \
              --root_path ${data_dir}/ark_data/dump/simu_data_chunk2000/$dataset \
              --out_path ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset \
              --split_num $nj
        grep "ns2" ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/feats.scp > ${data_dir}/ark_data/dump/simu_data_chunk2000/data/$dataset/feats_2spkr.scp
        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=${data_dir}/ark_data/dump/callhome/$dset
        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 ${data_dir}/ark_data/dump/callhome/data/$dset
        python local/gen_feats_scp.py \
              --root_path ${data_dir}/ark_data/dump/callhome/$dset \
              --out_path ${data_dir}/ark_data/dump/callhome/data/$dset \
              --split_num 1
        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

@@ -228,10 +222,10 @@
    python local/model_averaging.py ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb $models
fi

# Training on simulated all-speaker data with chunk_size=2000
# Training on simulated all-speaker data with chunk_size 2000
world_size=$gpu_num
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    echo "stage 3: Training on simulated all-speaker data with chunk_size=2000"
    echo "stage 3: Training on simulated all-speaker data with chunk_size 2000"
    mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}
    mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log
    INIT_FILE=${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/ddp_init
@@ -269,11 +263,11 @@
        wait
fi

# Training on callhome all-speaker data with chunk_size=2000
# Training on callhome all-speaker data with chunk_size 2000
world_size=$gpu_num
callhome_ave_id=avg${callhome_average_start}-${callhome_average_end}
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    echo "stage 4: Training on callhome all-speaker data with chunk_size=2000"
    echo "stage 4: Training on callhome all-speaker data with chunk_size 2000"
    mkdir -p ${exp_dir}/exp/${callhome_model_dir}
    mkdir -p ${exp_dir}/exp/${callhome_model_dir}/log
    INIT_FILE=${exp_dir}/exp/${callhome_model_dir}/ddp_init
@@ -325,6 +319,6 @@
        --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 ${callhome_feats_dir_chunk2000}/${callhome_valid_dataset}/rttm \
          -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