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FunASR/egs/callhome/eend_ola/run_test.sh
嘉渊 a7b3496039 update
2023-07-19 01:49:02 +08:00

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#!/usr/bin/env bash
. ./path.sh || exit 1;
# machines configuration
CUDA_VISIBLE_DEVICES="7"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
count=1
# general configuration
simu_feats_dir="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data/data"
simu_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/simu_data_chunk2000/data"
callhome_feats_dir_chunk2000="/nfs/wangjiaming.wjm/EEND_ARK_DATA/dump/callhome_chunk2000/data"
simu_train_dataset=train
simu_valid_dataset=dev
callhome_train_dataset=callhome1_allspk
callhome_valid_dataset=callhome2_allspk
callhome2_wav_scp_file=wav.scp
# model average
simu_average_2spkr_start=91
simu_average_2spkr_end=100
simu_average_allspkr_start=16
simu_average_allspkr_end=25
callhome_average_start=91
callhome_average_end=100
exp_dir="."
input_size=345
stage=5
stop_stage=5
# exp tag
tag="exp1"
. local/parse_options.sh || exit 1;
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail
simu_2spkr_diar_config=conf/train_diar_eend_ola_simu_2spkr.yaml
simu_allspkr_diar_config=conf/train_diar_eend_ola_simu_allspkr.yaml
simu_allspkr_chunk2000_diar_config=conf/train_diar_eend_ola_simu_allspkr_chunk2000.yaml
callhome_diar_config=conf/train_diar_eend_ola_callhome_chunk2000.yaml
simu_2spkr_model_dir="baseline_$(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 ] && [ ${stop_stage} -ge 0 ]; then
# echo "stage 0: Prepare data for training and inference"
# echo "The detail can be found in https://github.com/hitachi-speech/EEND"
# . ./local/
#fi
#
# Training on simulated two-speaker data
world_size=$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
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
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