run.sh

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+#!/bin/bash
+
+# Created on 2018/12
+# Author: Kaituo XU
+
+# -- START IMPORTANT
+# * If you have mixture wsj0 audio, modify `data` to your path that including tr, cv and tt.
+# * If you jsut have origin sphere format wsj0 , modify `wsj0_origin` to your path and
+# modify `wsj0_wav` to path that put output wav format wsj0, then read and run stage 1 part.
+# After that, modify `data` and run from stage 2.
+wsj0_origin=E:\PHD\data\WSJ0
+wsj0_wav=E:\PHD\data\wav
+data=E:\PHD\data\wav8k
+stage=0  # Modify this to control to start from which stage
+# -- END
+:<<!
+dumpdir=data  # directory to put generated json file
+
+# -- START Conv-TasNet Config
+train_dir=$dumpdir/tr
+valid_dir=$dumpdir/cv
+evaluate_dir=$dumpdir/tt
+separate_dir=$dumpdir/tt
+sample_rate=8000
+segment=4  # seconds
+cv_maxlen=6  # seconds
+# Network config
+N=256
+L=20
+B=256
+H=512
+P=3
+X=8
+R=4
+norm_type=gLN
+causal=0
+mask_nonlinear='relu'
+C=2
+# Training config
+use_cuda=1
+id=0
+epochs=100
+half_lr=1
+early_stop=0
+max_norm=5
+# minibatch
+shuffle=1
+batch_size=3
+num_workers=4
+# optimizer
+optimizer=adam
+lr=1e-3
+momentum=0
+l2=0
+# save and visualize
+checkpoint=0
+continue_from=""
+print_freq=10
+visdom=0
+visdom_epoch=0
+visdom_id="Conv-TasNet Training"
+# evaluate
+ev_use_cuda=0
+cal_sdr=1
+# -- END Conv-TasNet Config
+
+# exp tag
+tag="" # tag for managing experiments.
+
+ngpu=1  # always 1
+!
+. utils/parse_options.sh || exit 1;
+. ./cmd.sh
+. ./path.sh
+
+
+if [ $stage -le 0 ]; then
+  echo "Stage 0: Convert sphere format to wav format and generate mixture"
+  local/data_prepare.sh --data ${wsj0_origin} --wav_dir ${wsj0_wav}
+
+  echo "NOTE: You should generate mixture by yourself now.
+You can use tools/create-speaker-mixtures.zip which is download from
+http://www.merl.com/demos/deep-clustering/create-speaker-mixtures.zip
+If you don't have Matlab and want to use Octave, I suggest to replace
+all mkdir(...) in create_wav_2speakers.m with system(['mkdir -p '...])
+due to mkdir in Octave can not work in 'mkdir -p' way.
+e.g.:
+mkdir([output_dir16k '/' min_max{i_mm} '/' data_type{i_type}]);
+->
+system(['mkdir -p ' output_dir16k '/' min_max{i_mm} '/' data_type{i_type}]);"
+  exit 1
+fi
+
+:<<!
+if [ $stage -le 1 ]; then
+  echo "Stage 1: Generating json files including wav path and duration"
+  [ ! -d $dumpdir ] && mkdir $dumpdir
+  preprocess.py --in-dir $data --out-dir $dumpdir --sample-rate $sample_rate
+fi
+
+
+if [ -z ${tag} ]; then
+  expdir=exp/train_r${sample_rate}_N${N}_L${L}_B${B}_H${H}_P${P}_X${X}_R${R}_C${C}_${norm_type}_causal${causal}_${mask_nonlinear}_epoch${epochs}_half${half_lr}_norm${max_norm}_bs${batch_size}_worker${num_workers}_${optimizer}_lr${lr}_mmt${momentum}_l2${l2}_`basename $train_dir`
+else
+  expdir=exp/train_${tag}
+fi
+
+if [ $stage -le 2 ]; then
+  echo "Stage 2: Training"
+  ${cuda_cmd} --gpu ${ngpu} ${expdir}/train.log \
+    CUDA_VISIBLE_DEVICES="$id" \
+    train.py \
+    --train_dir $train_dir \
+    --valid_dir $valid_dir \
+    --sample_rate $sample_rate \
+    --segment $segment \
+    --cv_maxlen $cv_maxlen \
+    --N $N \
+    --L $L \
+    --B $B \
+    --H $H \
+    --P $P \
+    --X $X \
+    --R $R \
+    --C $C \
+    --norm_type $norm_type \
+    --causal $causal \
+    --mask_nonlinear $mask_nonlinear \
+    --use_cuda $use_cuda \
+    --epochs $epochs \
+    --half_lr $half_lr \
+    --early_stop $early_stop \
+    --max_norm $max_norm \
+    --shuffle $shuffle \
+    --batch_size $batch_size \
+    --num_workers $num_workers \
+    --optimizer $optimizer \
+    --lr $lr \
+    --momentum $momentum \
+    --l2 $l2 \
+    --save_folder ${expdir} \
+    --checkpoint $checkpoint \
+    --continue_from "$continue_from" \
+    --print_freq ${print_freq} \
+    --visdom $visdom \
+    --visdom_epoch $visdom_epoch \
+    --visdom_id "$visdom_id"
+fi
+
+
+if [ $stage -le 3 ]; then
+  echo "Stage 3: Evaluate separation performance"
+  ${decode_cmd} --gpu ${ngpu} ${expdir}/evaluate.log \
+    evaluate.py \
+    --model_path ${expdir}/final.pth.tar \
+    --data_dir $evaluate_dir \
+    --cal_sdr $cal_sdr \
+    --use_cuda $ev_use_cuda \
+    --sample_rate $sample_rate \
+    --batch_size $batch_size
+fi
+
+
+if [ $stage -le 4 ]; then
+  echo "Stage 4: Separate speech using Conv-TasNet"
+  separate_out_dir=${expdir}/separate
+  ${decode_cmd} --gpu ${ngpu} ${separate_out_dir}/separate.log \
+    separate.py \
+    --model_path ${expdir}/final.pth.tar \
+    --mix_json $separate_dir/mix.json \
+    --out_dir ${separate_out_dir} \
+    --use_cuda $ev_use_cuda \
+    --sample_rate $sample_rate \
+    --batch_size $batch_size
+fi
+!