From f8d1c79fe355efb18ae49e4363307dfec3ab89ce Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期一, 07 八月 2023 16:14:11 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
egs/callhome/eend_ola/run.sh | 324 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 324 insertions(+), 0 deletions(-)
diff --git a/egs/callhome/eend_ola/run.sh b/egs/callhome/eend_ola/run.sh
new file mode 100644
index 0000000..aa441bf
--- /dev/null
+++ b/egs/callhome/eend_ola/run.sh
@@ -0,0 +1,324 @@
+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="0"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+count=1
+
+# general configuration
+dump_cmd=utils/run.pl
+nj=64
+
+# feature configuration
+data_dir="./data"
+simu_feats_dir=$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_spkall
+callhome_valid_dataset=callhome2_spkall
+
+# model average
+simu_average_2spkr_start=91
+simu_average_2spkr_end=100
+simu_average_allspkr_start=16
+simu_average_allspkr_end=25
+callhome_average_start=91
+callhome_average_end=100
+
+exp_dir="."
+input_size=345
+stage=1
+stop_stage=5
+
+# exp tag
+tag="exp1"
+
+. local/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+simu_2spkr_diar_config=conf/train_diar_eend_ola_simu_2spkr.yaml
+simu_allspkr_diar_config=conf/train_diar_eend_ola_simu_allspkr.yaml
+simu_allspkr_chunk2000_diar_config=conf/train_diar_eend_ola_simu_allspkr_chunk2000.yaml
+callhome_diar_config=conf/train_diar_eend_ola_callhome_chunk2000.yaml
+simu_2spkr_model_dir="baseline_$(basename "${simu_2spkr_diar_config}" .yaml)_${tag}"
+simu_allspkr_model_dir="baseline_$(basename "${simu_allspkr_diar_config}" .yaml)_${tag}"
+simu_allspkr_chunk2000_model_dir="baseline_$(basename "${simu_allspkr_chunk2000_diar_config}" .yaml)_${tag}"
+callhome_model_dir="baseline_$(basename "${callhome_diar_config}" .yaml)_${tag}"
+
+# simulate mixture data for training and inference
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+ 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
+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=$gpu_num
+simu_2spkr_ave_id=avg${simu_average_2spkr_start}-${simu_average_2spkr_end}
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+ echo "stage 1: Training on simulated two-speaker data"
+ mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}
+ mkdir -p ${exp_dir}/exp/${simu_2spkr_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${simu_2spkr_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats_2spkr.scp" \
+ --resume true \
+ --output_dir ${exp_dir}/exp/${simu_2spkr_model_dir} \
+ --config $simu_2spkr_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_2spkr_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$simu_2spkr_model_dir/$simu_2spkr_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${simu_2spkr_model_dir}/{$simu_average_2spkr_start..$simu_average_2spkr_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb $models
+fi
+
+# Training on simulated all-speaker data
+world_size=$gpu_num
+simu_allspkr_ave_id=avg${simu_average_allspkr_start}-${simu_average_allspkr_end}
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+ echo "stage 2: Training on simulated all-speaker data"
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}
+ mkdir -p ${exp_dir}/exp/${simu_allspkr_model_dir}/log
+ INIT_FILE=${exp_dir}/exp/${simu_allspkr_model_dir}/ddp_init
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_2spkr_model_dir}/$simu_2spkr_ave_id.pb \
+ --output_dir ${exp_dir}/exp/${simu_allspkr_model_dir} \
+ --config $simu_allspkr_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$simu_allspkr_model_dir/$simu_allspkr_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${simu_allspkr_model_dir}/{$simu_average_allspkr_start..$simu_average_allspkr_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb $models
+fi
+
+# Training on simulated all-speaker data with chunk_size 2000
+world_size=$gpu_num
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+ echo "stage 3: Training on simulated all-speaker data with chunk_size 2000"
+ 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
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${simu_feats_dir_chunk2000} \
+ --train_set ${simu_train_dataset} \
+ --valid_set ${simu_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_allspkr_model_dir}/$simu_allspkr_ave_id.pb \
+ --output_dir ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir} \
+ --config $simu_allspkr_chunk2000_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+fi
+
+# Training on callhome all-speaker data with chunk_size 2000
+world_size=$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"
+ 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
+ if [ -f $INIT_FILE ];then
+ rm -f $INIT_FILE
+ fi
+ init_method=file://$(readlink -f $INIT_FILE)
+ echo "$0: init method is $init_method"
+ for ((i = 0; i < $gpu_num; ++i)); do
+ {
+ rank=$i
+ local_rank=$i
+ gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+ train.py \
+ --task_name diar \
+ --gpu_id $gpu_id \
+ --use_preprocessor false \
+ --input_size $input_size \
+ --data_dir ${callhome_feats_dir_chunk2000} \
+ --train_set ${callhome_train_dataset} \
+ --valid_set ${callhome_valid_dataset} \
+ --data_file_names "feats.scp" \
+ --resume true \
+ --init_param ${exp_dir}/exp/${simu_allspkr_chunk2000_model_dir}/1epoch.pb \
+ --output_dir ${exp_dir}/exp/${callhome_model_dir} \
+ --config $callhome_diar_config \
+ --ngpu $gpu_num \
+ --num_worker_count $count \
+ --dist_init_method $init_method \
+ --dist_world_size $world_size \
+ --dist_rank $rank \
+ --local_rank $local_rank 1> ${exp_dir}/exp/${callhome_model_dir}/log/train.log.$i 2>&1
+ } &
+ done
+ wait
+ echo "averaging model parameters into ${exp_dir}/exp/$callhome_model_dir/$callhome_ave_id.pb"
+ models=`eval echo ${exp_dir}/exp/${callhome_model_dir}/{$callhome_average_start..$callhome_average_end}epoch.pb`
+ python local/model_averaging.py ${exp_dir}/exp/${callhome_model_dir}/$callhome_ave_id.pb $models
+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
\ No newline at end of file
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