python/FunASR-XL.git

			@@ -8,8 +8,17 @@
			# [2] Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis, EMNLP 2022
			# We recommend you run this script stage by stage.

			# [developing] This recipe includes:
			# 1. simulating data with switchboard and NIST.
			# 2. training the model from scratch for 3 stages:
			# 2-1. pre-train on simu_swbd_sre
			# 2-2. train on simu_swbd_sre
			# 2-3. finetune on callhome1
			# 3. evaluating model with the results from the first stage EEND-OLA,
			# Finally, you will get a similar DER result claimed in the paper.

			# environment configuration
			kaldi_root=/home/neo.dzh/kaldi
			kaldi_root=

			if [ -z "${kaldi_root}" ]; then
			echo "We need kaldi to prepare dataset, extract fbank features, please install kaldi first and set kaldi_root."
			@@ -26,8 +35,8 @@
			fi

			# machines configuration
			gpu_devices="6,7"
			gpu_num=2
			gpu_devices="4,5,6,7" # for V100-16G, use 4 GPUs
			gpu_num=4
			count=1

			# general configuration
			@@ -39,6 +48,7 @@

			# dataset related
			data_root=
			callhome_root=path/to/NIST/LDC2001S97

			# experiment configuration
			lang=en
			@@ -115,7 +125,7 @@
			utils/fix_data_dir.sh ${datadir}/swbd_sre

			# 3. Prepare the Callhome portion of NIST SRE 2000.
			local/make_callhome.sh /nfs/wangjiaming.wjm/speech-data/NIST/LDC2001S97 ${datadir}/
			local/make_callhome.sh ${callhome_root} ${datadir}/

			fi

			@@ -417,7 +427,7 @@
			rank=$i
			local_rank=$i
			gpu_id=$(echo $gpu_devices \| cut -d',' -f$[$i+1])
			diar_train.py \
			python -m funasr.bin.diar_train \
			--gpu_id $gpu_id \
			--use_preprocessor false \
			--token_type char \
			@@ -565,7 +575,7 @@
			rank=$i
			local_rank=$i
			gpu_id=$(echo $gpu_devices \| cut -d',' -f$[$i+1])
			diar_train.py \
			python -m funasr.bin.diar_train \
			--gpu_id $gpu_id \
			--use_preprocessor false \
			--token_type char \
			@@ -710,7 +720,7 @@
			rank=$i
			local_rank=$i
			gpu_id=$(echo $gpu_devices \| cut -d',' -f$[$i+1])
			diar_train.py \
			python -m funasr.bin.diar_train \
			--gpu_id $gpu_id \
			--use_preprocessor false \
			--token_type char \
			@@ -872,10 +882,11 @@

			${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/sv_inference.JOB.log \
			python -m funasr.bin.sv_inference_launch \
			--njob ${njob} \
			--batch_size 1 \
			--ngpu "${_ngpu}" \
			--gpuid_list ${gpuid_list} \
			--data_path_and_name_and_type "${key_file},speech,kaldi_ark" \
			--data_path_and_name_and_type "${key_file},speech,sound" \
			--key_file "${_logdir}"/keys.JOB.scp \
			--sv_train_config ${sv_exp_dir}/sv.yaml \
			--sv_model_file ${sv_exp_dir}/sv.pth \
			@@ -941,62 +952,3 @@
			echo "Done."
			done
			fi


			if [ ${stage} -le 30 ] && [ ${stop_stage} -ge 30 ]; then
			echo "stage 30: training phase 1, pretraining on simulated data"
			world_size=$gpu_num # run on one machine
			mkdir -p ${expdir}/${model_dir}
			mkdir -p ${expdir}/${model_dir}/log
			mkdir -p /tmp/${model_dir}
			INIT_FILE=/tmp/${model_dir}/ddp_init
			if [ -f $INIT_FILE ];then
			rm -f $INIT_FILE
			fi
			init_opt=""
			if [ ! -z "${init_param}" ]; then
			init_opt="--init_param ${init_param}"
			echo ${init_opt}
			fi

			freeze_opt=""
			if [ ! -z "${freeze_param}" ]; then
			freeze_opt="--freeze_param ${freeze_param}"
			echo ${freeze_opt}
			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 $gpu_devices \| cut -d',' -f$[$i+1])
			diar_train.py \
			--gpu_id $gpu_id \
			--use_preprocessor false \
			--token_type char \
			--token_list $token_list \
			--dataset_type large \
			--train_data_file ${datadir}/${train_set}/dumped_files/data_file.list \
			--valid_data_file ${datadir}/${valid_set}/dumped_files/data_file.list \
			--init_param ${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/sv.pth:encoder:encoder \
			--freeze_param encoder \
			${init_opt} \
			${freeze_opt} \
			--ignore_init_mismatch true \
			--resume true \
			--output_dir ${expdir}/${model_dir} \
			--config $train_config \
			--ngpu $gpu_num \
			--num_worker_count $count \
			--multiprocessing_distributed true \
			--dist_init_method $init_method \
			--dist_world_size $world_size \
			--dist_rank $rank \
			--local_rank $local_rank 1> ${expdir}/${model_dir}/log/train.log.$i 2>&1
			} &
			done
			echo "Training log can be found at ${expdir}/${model_dir}/log/train.log.*"
			wait
			fi