#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="7"
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gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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count=1
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# general configuration
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simu_feats_dir="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data/data"
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simu_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data_chunk2000/data"
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callhome_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/callhome_chunk2000/data"
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simu_train_dataset=train
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simu_valid_dataset=dev
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callhome_train_dataset=callhome1_allspk
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callhome_valid_dataset=callhome2_allspk
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callhome2_wav_scp_file=wav.scp
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# model average
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simu_average_2spkr_start=91
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simu_average_2spkr_end=100
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simu_average_allspkr_start=16
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simu_average_allspkr_end=25
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callhome_average_start=91
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callhome_average_end=100
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exp_dir="."
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input_size=345
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stage=-1
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stop_stage=-1
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# exp tag
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tag="exp_fix"
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. local/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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simu_2spkr_diar_config=conf/train_diar_eend_ola_simu_2spkr.yaml
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simu_allspkr_diar_config=conf/train_diar_eend_ola_simu_allspkr.yaml
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simu_allspkr_chunk2000_diar_config=conf/train_diar_eend_ola_simu_allspkr_chunk2000.yaml
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callhome_diar_config=conf/train_diar_eend_ola_callhome_chunk2000.yaml
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simu_2spkr_model_dir="baseline_$(basename "${simu_2spkr_diar_config}" .yaml)_${tag}"
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simu_allspkr_model_dir="baseline_$(basename "${simu_allspkr_diar_config}" .yaml)_${tag}"
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simu_allspkr_chunk2000_model_dir="baseline_$(basename "${simu_allspkr_chunk2000_diar_config}" .yaml)_${tag}"
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callhome_model_dir="baseline_$(basename "${callhome_diar_config}" .yaml)_${tag}"
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# simulate mixture data for training and inference
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage 0: Simulate mixture data for training and inference"
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echo "The detail can be found in https://github.com/hitachi-speech/EEND"
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echo "Before running this step, you should download and compile kaldi and set KALDI_ROOT in this script and path.sh"
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echo "This stage may take a long time, please waiting..."
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KALDI_ROOT=
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ln -s $KALDI_ROOT/egs/wsj/s5/steps steps
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ln -s $KALDI_ROOT/egs/wsj/s5/utils utils
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. local/run_prepare_shared_eda.sh
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fi
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## Prepare data for training and inference
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#if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# echo "stage 0: Prepare data for training and inference"
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# echo "The detail can be found in https://github.com/hitachi-speech/EEND"
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# . ./local/
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#fi
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#
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# Training on simulated two-speaker data
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world_size=$gpu_num
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simu_2spkr_ave_id=avg${simu_average_2spkr_start}-${simu_average_2spkr_end}
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Training on simulated two-speaker data"
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mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}
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mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}/log
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INIT_FILE=${exp_dir}/exp/${simu_2spkr_model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name diar \
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--gpu_id $gpu_id \
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--use_preprocessor false \
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--input_size $input_size \
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--data_dir ${simu_feats_dir} \
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--train_set ${simu_train_dataset} \
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--valid_set ${simu_valid_dataset} \
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--data_file_names "feats_2spkr.scp" \
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--resume true \
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--output_dir ${exp_dir}/exp/${simu_2spkr_model_dir} \
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--config $simu_2spkr_diar_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${simu_2spkr_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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echo "averaging model parameters into ${exp_dir}/exp/$simu_2spkr_model_dir/$simu_2spkr_ave_id.pb"
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models=`eval echo ${exp_dir}/exp/${simu_2spkr_model_dir}/{$simu_average_2spkr_start..$simu_average_2spkr_end}epoch.pb`
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python local/model_averaging.py ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb $models
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fi
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# Training on simulated all-speaker data
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world_size=$gpu_num
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simu_allspkr_ave_id=avg${simu_average_allspkr_start}-${simu_average_allspkr_end}
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Training on simulated all-speaker data"
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mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}
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mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}/log
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INIT_FILE=${exp_dir}/exp/${simu_allspkr_model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name diar \
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--gpu_id $gpu_id \
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--use_preprocessor false \
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--input_size $input_size \
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--data_dir ${simu_feats_dir} \
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--train_set ${simu_train_dataset} \
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--valid_set ${simu_valid_dataset} \
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--data_file_names "feats.scp" \
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--resume true \
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--init_param ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb \
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--output_dir ${exp_dir}/exp/${simu_allspkr_model_dir} \
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--config $simu_allspkr_diar_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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echo "averaging model parameters into ${exp_dir}/exp/$simu_allspkr_model_dir/$simu_allspkr_ave_id.pb"
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models=`eval echo ${exp_dir}/exp/${simu_allspkr_model_dir}/{$simu_average_allspkr_start..$simu_average_allspkr_end}epoch.pb`
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python local/model_averaging.py ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb $models
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fi
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# Training on simulated all-speaker data with chunk_size=2000
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world_size=$gpu_num
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: Training on simulated all-speaker data with chunk_size=2000"
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mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}
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mkdir -p ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log
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INIT_FILE=${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name diar \
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--gpu_id $gpu_id \
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--use_preprocessor false \
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--input_size $input_size \
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--data_dir ${simu_feats_dir_chunk2000} \
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--train_set ${simu_train_dataset} \
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--valid_set ${simu_valid_dataset} \
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--data_file_names "feats.scp" \
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--resume true \
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--init_param ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb \
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--output_dir ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir} \
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--config $simu_allspkr_chunk2000_diar_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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fi
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# Training on callhome all-speaker data with chunk_size=2000
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world_size=$gpu_num
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callhome_ave_id=avg${callhome_average_start}-${callhome_average_end}
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: Training on callhome all-speaker data with chunk_size=2000"
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mkdir -p ${exp_dir}/exp/${callhome_model_dir}
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mkdir -p ${exp_dir}/exp/${callhome_model_dir}/log
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INIT_FILE=${exp_dir}/exp/${callhome_model_dir}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $gpu_num; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name diar \
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--gpu_id $gpu_id \
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--use_preprocessor false \
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--input_size $input_size \
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--data_dir ${callhome_feats_dir_chunk2000} \
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--train_set ${callhome_train_dataset} \
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--valid_set ${callhome_valid_dataset} \
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--data_file_names "feats.scp" \
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--resume true \
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--init_param ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/1epoch.pb \
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--output_dir ${exp_dir}/exp/${callhome_model_dir} \
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--config $callhome_diar_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/exp/${callhome_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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echo "averaging model parameters into ${exp_dir}/exp/$callhome_model_dir/$callhome_ave_id.pb"
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models=`eval echo ${exp_dir}/exp/${callhome_model_dir}/{$callhome_average_start..$callhome_average_end}epoch.pb`
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python local/model_averaging.py ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb $models
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fi
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## inference
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#if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# echo "Inference"
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# mkdir -p ${exp_dir}/exp/${callhome_model_dir}/inference/log
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# CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python local/infer.py \
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# --config_file ${exp_dir}/exp/${callhome_model_dir}/config.yaml \
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# --model_file ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb \
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# --output_rttm_file ${exp_dir}/exp/${callhome_model_dir}/inference/rttm \
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# --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
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#fi
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