From 47343b5c2f4e1256f60f46d8da0aa2e5de39b6c7 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期六, 05 八月 2023 17:53:08 +0800
Subject: [PATCH] init repo
---
/dev/null | 331 -------------------------------------------------------
1 files changed, 0 insertions(+), 331 deletions(-)
diff --git a/egs/callhome/eend_ola/run_test.sh b/egs/callhome/eend_ola/run_test.sh
deleted file mode 100644
index 36ba1e7..0000000
--- a/egs/callhome/eend_ola/run_test.sh
+++ /dev/null
@@ -1,331 +0,0 @@
-#!/usr/bin/env bash
-
-. ./path.sh || exit 1;
-
-# machines configuration
-CUDA_VISIBLE_DEVICES="0"
-gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-count=1
-
-# general configuration
-dump_cmd=utils/run.pl
-nj=64
-
-# feature configuration
-data_dir="./data"
-simu_feats_dir="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data/data"
-simu_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data_chunk2000/data"
-callhome_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/callhome_chunk2000/data"
-simu_train_dataset=train
-simu_valid_dataset=dev
-callhome_train_dataset=callhome1_allspk
-callhome_valid_dataset=callhome2_allspk
-callhome2_wav_scp_file=wav.scp
-
-# model average
-simu_average_2spkr_start=91
-simu_average_2spkr_end=100
-simu_average_allspkr_start=16
-simu_average_allspkr_end=25
-callhome_average_start=91
-callhome_average_end=100
-
-exp_dir="."
-input_size=345
-stage=0
-stop_stage=0
-
-# exp tag
-tag="exp1"
-
-. local/parse_options.sh || exit 1;
-
-# Set bash to 'debug' mode, it will exit on :
-# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
-set -e
-set -u
-set -o pipefail
-
-simu_2spkr_diar_config=conf/train_diar_eend_ola_simu_2spkr.yaml
-simu_allspkr_diar_config=conf/train_diar_eend_ola_simu_allspkr.yaml
-simu_allspkr_chunk2000_diar_config=conf/train_diar_eend_ola_simu_allspkr_chunk2000.yaml
-callhome_diar_config=conf/train_diar_eend_ola_callhome_chunk2000.yaml
-simu_2spkr_model_dir="baseline_$(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
-# 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
-# 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
-# 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
-# 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
- python local/gen_feats_scp.py \
- --root_path ${data_dir}/ark_data/dump/callhome_chunk2000/$dset \
- --out_path ${data_dir}/ark_data/dump/callhome_chunk2000/data/$dset \
- --split_num 1
- 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 ${callhome_feats_dir_chunk2000}/${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
\ No newline at end of file
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