{"action":"created","comment":{"url":"https://api.github.com/repos/espnet/espnet/pulls/comments/177617916","pull_request_review_id":107519795,"id":177617916,"diff_hunk":"@@ -0,0 +1,354 @@\n+#!/bin/bash\n+\n+# Copyright 2017 Johns Hopkins University (Shinji Watanabe)\n+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)\n+\n+. ./path.sh\n+. ./cmd.sh\n+\n+# general configuration\n+backend=chainer\n+stage=0        # start from 0 if you need to start from data preparation\n+gpu=-1         # use 0 when using GPU on slurm/grid engine, otherwise -1\n+debugmode=1\n+dumpdir=dump   # directory to dump full features\n+N=0            # number of minibatches to be used (mainly for debugging). \"0\" uses all minibatches.\n+verbose=0      # verbose option\n+resume=        # Resume the training from snapshot\n+\n+# feature configuration\n+do_delta=false # true when using CNN\n+\n+# network archtecture\n+# encoder related\n+etype=blstmp     # encoder architecture type\n+elayers=6\n+eunits=320\n+eprojs=320\n+subsample=1_2_2_1_1 # skip every n frame from input to nth layers\n+# loss related\n+ctctype=chainer\n+# decoder related\n+dlayers=1\n+dunits=300\n+# attention related\n+atype=location\n+aconv_chans=10\n+aconv_filts=100\n+\n+# hybrid CTC/attention\n+mtlalpha=0.5\n+\n+# minibatch related\n+batchsize=10\n+maxlen_in=800  # if input length  > maxlen_in, batchsize is automatically reduced\n+maxlen_out=150 # if output length > maxlen_out, batchsize is automatically reduced\n+\n+# optimization related\n+opt=adadelta\n+epochs=15\n+\n+# decoding parameter\n+beam_size=20\n+penalty=0\n+maxlenratio=0.8\n+minlenratio=0.3\n+recog_model=acc.best # set a model to be used for decoding: 'acc.best' or 'loss.best'\n+\n+# me2e\n+cmvn=data-fbank/tr05_multi_noisy/cmvn.ark\n+melmat=exp/make_melmat/melmat.ark\n+trainmode=\"noisy+enhan\" # training mode\n+recogmode=\"enhan\" # decoding mode\n+\n+# data\n+chime4_data=/export/corpora4/CHiME4/CHiME3 # JHU setup\n+\n+# exp tag\n+tag=\"\" # tag for managing experiments.\n+\n+. utils/parse_options.sh || exit 1;\n+\n+. ./path.sh \n+. ./cmd.sh \n+\n+# Set bash to 'debug' mode, it will exit on :\n+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',\n+set -e\n+set -u\n+set -o pipefail\n+\n+train_set=tr05_multi_noisy\n+\n+if [ ${stage} -le 0 ]; then\n+    ### Task dependent. You have to make the following data preparation part by yourself.\n+    ### But you can utilize Kaldi recipes in most cases\n+    echo \"stage 0: Data preparation\"\n+    wsj0_data=${chime4_data}/data/WSJ0\n+    local/clean_wsj0_data_prep.sh ${wsj0_data}\n+    local/clean_chime4_format_data.sh\n+    echo \"beamforming for multichannel cases\"\n+    local/run_beamform_2ch_track.sh --cmd \"${train_cmd}\" --nj 20 \\\n+        ${chime4_data}/data/audio/16kHz/isolated_2ch_track enhan/beamformit_2mics\n+    local/run_beamform_6ch_track.sh --cmd \"${train_cmd}\" --nj 20 \\\n+        ${chime4_data}/data/audio/16kHz/isolated_6ch_track enhan/beamformit_5mics\n+    echo \"prepartion for chime4 data\"\n+    local/real_noisy_chime4_data_prep.sh ${chime4_data}\n+    local/simu_noisy_chime4_data_prep.sh ${chime4_data}\n+    echo \"test data for 1ch track\"\n+    local/real_enhan_chime4_data_prep.sh isolated_1ch_track ${chime4_data}/data/audio/16kHz/isolated_1ch_track\n+    local/simu_enhan_chime4_data_prep.sh isolated_1ch_track ${chime4_data}/data/audio/16kHz/isolated_1ch_track\n+    echo \"test data for 2ch track\"\n+    local/real_enhan_chime4_data_prep.sh beamformit_2mics ${PWD}/enhan/beamformit_2mics\n+    local/simu_enhan_chime4_data_prep.sh beamformit_2mics ${PWD}/enhan/beamformit_2mics\n+    echo \"test data for 6ch track\"\n+    local/real_enhan_chime4_data_prep.sh beamformit_5mics ${PWD}/enhan/beamformit_5mics\n+    local/simu_enhan_chime4_data_prep.sh beamformit_5mics ${PWD}/enhan/beamformit_5mics\n+fi\n+\n+if [ ${stage} -le 1 ]; then\n+    ### Task dependent. You have to design training and dev sets by yourself.\n+    ### But you can utilize Kaldi recipes in most cases\n+    echo \"stage 1: Feature Generation\"\n+    # Generate the fbank features; by default 80-dimensional fbanks to obtain cmvn statistics\n+    fbankdir=fbank\n+    tasks=\"tr05_real_noisy tr05_simu_noisy\"\n+    for x in ${tasks}; do\n+        utils/copy_data_dir.sh data/${x} data-fbank/${x}\n+        steps/make_fbank.sh --nj 8 --cmd \"${train_cmd}\" data-fbank/${x} exp/make_fbank/${x} ${fbankdir}\n+    done\n+\n+    echo \"combine real and simulation data\"\n+    utils/combine_data.sh data-fbank/${train_set} data-fbank/tr05_simu_noisy data-fbank/tr05_real_noisy\n+\n+    # compute global CMVN\n+    compute-cmvn-stats scp:data-fbank/${train_set}/feats.scp data-fbank/${train_set}/cmvn.ark\n+\n+    # compute Mel-filterbank matrix\n+    fbank_config=conf/fbank.conf\n+    meldir=exp/make_melmat; mkdir -p ${meldir}\n+    melmat.py --config=${fbank_config} ${meldir}/melmat.ark\n+\n+    # Generate the stft features; by default 257-dimensional stfts\n+    channels=\"1 2 3 4 5 6\"\n+\n+    # noisy\n+    tasks=\"\\\n+    tr05_real_noisy tr05_simu_noisy dt05_real_noisy dt05_simu_noisy et05_real_noisy et05_simu_noisy \\\n+    dt05_real_isolated_1ch_track dt05_simu_isolated_1ch_track et05_real_isolated_1ch_track et05_simu_isolated_1ch_track \\\n+    \"\n+    stftdir=stft\n+    for x in ${tasks}; do\n+        utils/copy_data_dir.sh data/${x} data-stft/${x}\n+        local/make_stft.sh --nj 8 --cmd \"${train_cmd}\" data-stft/${x} exp/make_stft/${x} ${stftdir}\n+    done\n+\n+    echo \"combine real and simulation data\"\n+    utils/combine_data.sh data-stft/tr05_multi_noisy data-stft/tr05_real_noisy data-stft/tr05_simu_noisy\n+    utils/combine_data.sh data-stft/dt05_multi_noisy data-stft/dt05_real_noisy data-stft/dt05_simu_noisy\n+    utils/combine_data.sh data-stft/dt05_multi_isolated_1ch_track data-stft/dt05_real_isolated_1ch_track data-stft/dt05_simu_isolated_1ch_track\n+\n+    # enhan\n+    tasks=\"\\\n+    tr05_real_noisy tr05_simu_noisy dt05_real_noisy dt05_simu_noisy et05_real_noisy et05_simu_noisy \\\n+    \"\n+    stftdir=stft\n+    for x in ${tasks}; do\n+        for ch in ${channels}; do\n+            utils/copy_data_dir.sh data/${x} data-stft/${x}_ch${ch}\n+            for f in text utt2spk wav.scp; do\n+                cat data-stft/${x}_ch${ch}/${f} | grep CH${ch} > data-stft/${x}_ch${ch}/${f}.tmp\n+                cp -a data-stft/${x}_ch${ch}/${f}.tmp data-stft/${x}_ch${ch}/${f}\n+                rm data-stft/${x}_ch${ch}/${f}.tmp\n+\n+                sed -i -e \"s/\\.CH${ch}_REAL//g\" data-stft/${x}_ch${ch}/${f}\n+                sed -i -e \"s/\\.CH${ch}_SIMU//g\" data-stft/${x}_ch${ch}/${f}\n+            done\n+            utils/utt2spk_to_spk2utt.pl data-stft/${x}_ch${ch}/utt2spk > data-stft/${x}_ch${ch}/spk2utt\n+            local/make_stft.sh --nj 8 --cmd \"${train_cmd}\" data-stft/${x}_ch${ch} exp/make_stft/${x}_ch${ch} ${stftdir}\n+        done\n+    done\n+\n+    echo \"combine real and simulation data\"\n+    for ch in ${channels}; do\n+        utils/combine_data.sh data-stft/tr05_multi_noisy_ch${ch} data-stft/tr05_real_noisy_ch${ch} data-stft/tr05_simu_noisy_ch${ch}\n+        utils/combine_data.sh data-stft/dt05_multi_noisy_ch${ch} data-stft/dt05_real_noisy_ch${ch} data-stft/dt05_simu_noisy_ch${ch}\n+    done\n+\n+fi\n+\n+dict=data/lang_1char/${train_set}_units.txt\n+echo \"dictionary: ${dict}\"\n+nlsyms=data/lang_1char/non_lang_syms.txt\n+if [ ${stage} -le 2 ]; then\n+    ### Task dependent. You have to check non-linguistic symbols used in the corpus.\n+    echo \"stage 2: Dictionary and Json Data Preparation\"\n+    mkdir -p data/lang_1char/\n+\n+    echo \"make a non-linguistic symbol list\"\n+    cut -f 2- data-stft/${train_set}/text | tr \" \" \"\\n\" | sort | uniq | grep \"<\" > ${nlsyms}\n+    cat ${nlsyms}\n+\n+    echo \"make a dictionary\"\n+    echo \"<unk> 1\" > ${dict} # <unk> must be 1, 0 will be used for \"blank\" in CTC\n+    text2token.py -s 1 -n 1 -l ${nlsyms} data-stft/${train_set}/text | cut -f 2- -d\" \" | tr \" \" \"\\n\" \\\n+    | sort | uniq | grep -v -e '^\\s*$' | awk '{print $0 \" \" NR+1}' >> ${dict}\n+    wc -l ${dict}\n+\n+    echo \"make json files\"\n+    # noisy\n+    data2json.sh --feat data-stft/tr05_multi_noisy/feats.scp --nlsyms ${nlsyms} \\\n+         data-stft/tr05_multi_noisy ${dict} > data-stft/tr05_multi_noisy/data.json\n+    data2json.sh --feat data-stft/dt05_multi_noisy/feats.scp --nlsyms ${nlsyms} \\\n+         data-stft/dt05_multi_noisy ${dict} > data-stft/dt05_multi_noisy/data.json\n+    data2json.sh --feat data-stft/dt05_multi_isolated_1ch_track/feats.scp --nlsyms ${nlsyms} \\\n+         data-stft/dt05_multi_isolated_1ch_track ${dict} > data-stft/dt05_multi_isolated_1ch_track/data.json\n+\n+    # enhan\n+    for ch in ${channels}; do\n+        data2json.sh --feat data-stft/tr05_multi_noisy_ch${ch}/feats.scp --nlsyms ${nlsyms} \\\n+             data-stft/tr05_multi_noisy_ch${ch} ${dict} > data-stft/tr05_multi_noisy_ch${ch}/data.json\n+        data2json.sh --feat data-stft/dt05_multi_noisy_ch${ch}/feats.scp --nlsyms ${nlsyms} \\\n+             data-stft/dt05_multi_noisy_ch${ch} ${dict} > data-stft/dt05_multi_noisy_ch${ch}/data.json\n+    done\n+fi\n+\n+\n+if [ -z ${tag} ]; then\n+    expdir=exp/${trainmode}_${train_set}_${etype}_e${elayers}_subsample${subsample}_unit${eunits}_proj${eprojs}_d${dlayers}_unit${dunits}_${atype}_aconvc${aconv_chans}_aconvf${aconv_filts}_mtlalpha${mtlalpha}_${opt}_bs${batchsize}_mli${maxlen_in}_mlo${maxlen_out}\n+    if ${do_delta}; then\n+        echo \"delta feature is not supported\"\n+        exit\n+#        expdir=${expdir}_delta\n+    fi\n+else\n+    expdir=exp/${train_set}_${tag}\n+fi\n+mkdir -p ${expdir}\n+\n+# noisy\n+train_feat_noisy=\"scp:data-stft/tr05_multi_noisy/feats.scp\"\n+valid_feat_noisy=\"scp:data-stft/dt05_multi_isolated_1ch_track/feats.scp\"\n+\n+# enhan\n+train_feat_enhan=\"\"\n+valid_feat_enhan=\"\"\n+stft_ch=\"1 3 4 5 6\"\n+for ch in ${stft_ch}; do\n+    train_feat_enhan=\"${train_feat_enhan} scp:data-stft/tr05_multi_noisy_ch${ch}/feats.scp\"\n+    valid_feat_enhan=\"${valid_feat_enhan} scp:data-stft/dt05_multi_noisy_ch${ch}/feats.scp\"\n+done\n+\n+recog_set=\"\\\n+dt05_real dt05_simu et05_real et05_simu\n+\"\n+\n+if [ ${stage} -le 3 ]; then\n+    echo \"stage 3: Network Training\"\n+    ${cuda_cmd} ${expdir}/train.log \\\n+        asr_mc_train.py \\\n+        --gpu ${gpu} \\\n+        --backend ${backend} \\\n+        --outdir ${expdir}/results \\\n+        --debugmode ${debugmode} \\\n+        --dict ${dict} \\\n+        --debugdir ${expdir} \\\n+        --minibatches ${N} \\\n+        --verbose ${verbose} \\\n+        --resume ${resume} \\\n+        --train-feat-noisy ${train_feat_noisy} \\\n+        --valid-feat-noisy ${valid_feat_noisy} \\\n+        --train-label-noisy data-stft/tr05_multi_noisy/data.json \\\n+        --valid-label-noisy data-stft/dt05_multi_isolated_1ch_track/data.json \\\n+        --train-feat-enhan ${train_feat_enhan} \\\n+        --valid-feat-enhan ${valid_feat_enhan} \\\n+        --train-label-enhan data-stft/tr05_multi_noisy_ch1/data.json \\\n+        --valid-label-enhan data-stft/dt05_multi_noisy_ch1/data.json \\\n+        --etype ${etype} \\\n+        --elayers ${elayers} \\\n+        --eunits ${eunits} \\\n+        --eprojs ${eprojs} \\\n+        --subsample ${subsample} \\\n+        --ctc_type ${ctctype} \\\n+        --dlayers ${dlayers} \\\n+        --dunits ${dunits} \\\n+        --atype ${atype} \\\n+        --aconv-chans ${aconv_chans} \\\n+        --aconv-filts ${aconv_filts} \\\n+        --mtlalpha ${mtlalpha} \\\n+        --batch-size ${batchsize} \\\n+        --maxlen-in ${maxlen_in} \\\n+        --maxlen-out ${maxlen_out} \\\n+        --opt ${opt} \\\n+        --epochs ${epochs} \\\n+        --melmat ${melmat} \\\n+        --cmvn ${cmvn} \\\n+        --mode ${trainmode}\n+fi\n+\n+if [ ${stage} -le 4 ]; then\n+    echo \"stage 4: Decoding\"\n+#    nj=32","path":"egs/chime4/asr1_mc/run.sh","position":291,"original_position":291,"commit_id":"4e3e62b55b37d21c7913566785ed447c352bd304","original_commit_id":"4e3e62b55b37d21c7913566785ed447c352bd304","user":{"login":"sw005320","id":11741550,"avatar_url":"https://avatars0.githubusercontent.com/u/11741550?v=4","gravatar_id":"","url":"https://api.github.com/users/sw005320","html_url":"https://github.com/sw005320","followers_url":"https://api.github.com/users/sw005320/followers","following_url":"https://api.github.com/users/sw005320/following{/other_user}","gists_url":"https://api.github.com/users/sw005320/gists{/gist_id}","starred_url":"https://api.github.com/users/sw005320/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sw005320/subscriptions","organizations_url":"https://api.github.com/users/sw005320/orgs","repos_url":"https://api.github.com/users/sw005320/repos","events_url":"https://api.github.com/users/sw005320/events{/privacy}","received_events_url":"https://api.github.com/users/sw005320/received_events","type":"User","site_admin":false},"body":"Please remove the comment.","created_at":"2018-03-28T01:28:52Z","updated_at":"2018-03-28T01:35:19Z","html_url":"https://github.com/espnet/espnet/pull/120#discussion_r177617916","pull_request_url":"https://api.github.com/repos/espnet/espnet/pulls/120","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/espnet/espnet/pulls/comments/177617916"},"html":{"href":"https://github.com/espnet/espnet/pull/120#discussion_r177617916"},"pull_request":{"href":"https://api.github.com/repos/espnet/espnet/pulls/120"}}},"pull_request":{"url":"https://api.github.com/repos/espnet/espnet/pulls/120","id":175617786,"html_url":"https://github.com/espnet/espnet/pull/120","diff_url":"https://github.com/espnet/espnet/pull/120.diff","patch_url":"https://github.com/espnet/espnet/pull/120.patch","issue_url":"https://api.github.com/repos/espnet/espnet/issues/120","number":120,"state":"open","locked":false,"title":"added multichannel end-to-end 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